From e203e01cb8cc787c02170099aa1c5ff867491cc0 Mon Sep 17 00:00:00 2001 From: "v-honsong@microsoft.com" Date: Fri, 18 Oct 2024 17:26:59 -0700 Subject: [PATCH 1/3] Updata ElasTST --- .gitignore | 8 + README.md | 375 +--- checkpoints/.gitignore | 5 - checkpoints/README.md | 30 - config/ablation_study/elastst_abs_pe.yaml | 54 + .../ablation_study/elastst_learnable_pe.yaml | 53 + .../ablation_study/elastst_random_sample.yaml | 55 + config/ablation_study/elastst_trope.yaml | 52 + config/ablation_study/elastst_wo_mask.yaml | 52 + config/ablation_study/elastst_wo_patch.yaml | 52 + config/ablation_study/elastst_wo_pe.yaml | 52 + config/ablation_study/elastst_wo_trope.yaml | 52 + .../electricity_ltsf}/autoformer.yaml | 10 +- .../electricity_ltsf/dlinear.yaml | 0 .../baselines/electricity_ltsf/elastst.yaml | 53 + .../electricity_ltsf/patchtst.yaml | 0 .../electricity_ltsf/transformer_enc.yaml | 43 + .../baselines/electricity_ltsf/tsmixer.yaml | 37 + .../etth1/autoformer.yaml} | 25 +- config/{ltsf => baselines}/etth1/dlinear.yaml | 0 config/baselines/etth1/elastst.yaml | 53 + .../{ltsf => baselines}/etth1/patchtst.yaml | 0 .../etth1/transformer_enc.yaml} | 8 +- .../etth1/tsmixer.yaml} | 19 +- config/baselines/etth2/autoformer.yaml | 43 + config/{ltsf => baselines}/etth2/dlinear.yaml | 0 config/baselines/etth2/elastst.yaml | 53 + .../{ltsf => baselines}/etth2/patchtst.yaml | 0 .../etth2/transformer_enc.yaml} | 28 +- .../etth2/tsmixer.yaml} | 19 +- config/baselines/ettm1/autoformer.yaml | 43 + config/{ltsf => baselines}/ettm1/dlinear.yaml | 0 config/baselines/ettm1/elastst.yaml | 53 + .../{ltsf => baselines}/ettm1/patchtst.yaml | 0 .../ettm1/transformer_enc.yaml} | 33 +- .../ettm1/tsmixer.yaml} | 16 +- config/baselines/ettm2/autoformer.yaml | 43 + config/{ltsf => baselines}/ettm2/dlinear.yaml | 0 config/baselines/ettm2/elastst.yaml | 53 + .../{ltsf => baselines}/ettm2/patchtst.yaml | 0 config/baselines/ettm2/transformer_enc.yaml | 43 + .../ettm2/tsmixer.yaml} | 21 +- 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config/tsfm/forecastpfn.yaml | 29 - config/tsfm/lag_llama.yaml | 30 - config/tsfm/moirai.yaml | 31 - .../moirai/context_5000/electricity_ltsf.yaml | 33 - .../moirai/context_5000/electricity_nips.yaml | 33 - config/tsfm/moirai/context_5000/etth1.yaml | 32 - config/tsfm/moirai/context_5000/etth2.yaml | 32 - config/tsfm/moirai/context_5000/ettm1.yaml | 32 - config/tsfm/moirai/context_5000/ettm2.yaml | 32 - .../context_5000/exchange_rate_nips.yaml | 33 - .../tsfm/moirai/context_5000/solar_nips.yaml | 33 - .../moirai/context_5000/weather_ltsf.yaml | 33 - .../moirai/context_96/electricity_ltsf.yaml | 33 - .../moirai/context_96/electricity_nips.yaml | 33 - config/tsfm/moirai/context_96/etth1.yaml | 33 - config/tsfm/moirai/context_96/etth2.yaml | 33 - config/tsfm/moirai/context_96/ettm1.yaml | 33 - config/tsfm/moirai/context_96/ettm2.yaml | 33 - .../moirai/context_96/exchange_rate_nips.yaml | 33 - config/tsfm/moirai/context_96/solar_nips.yaml | 33 - .../tsfm/moirai/context_96/weather_ltsf.yaml | 33 - config/tsfm/timer.yaml | 28 - config/tsfm/timesfm.yaml | 31 - config/tsfm/tinytimemixer.yaml | 25 - config/tsfm/units.yaml | 28 - docs/benchmark/FOUNDATION_MODEL.md | 32 - docs/benchmark/README.md | 23 - docs/benchmark/figs/FM_dataset.jpg | Bin 268047 -> 0 bytes docs/benchmark/figs/FM_summary.jpg | Bin 301553 -> 0 bytes docs/benchmark/figs/fm_short_term.jpg | Bin 262161 -> 0 bytes docs/benchmark/figs/fm_var_hor.jpg | Bin 1242920 -> 0 bytes docs/benchmark/figs/long_bench.jpg | Bin 3101238 -> 0 bytes docs/benchmark/figs/short_bench.jpg | Bin 885625 -> 0 bytes docs/figs/framework.png | Bin 0 -> 796502 bytes exps/.gitignore | 2 - probts/data/data_manager.py | 307 +-- probts/data/data_module.py | 101 +- probts/data/data_wrapper.py | 60 + probts/data/ltsf_datasets.py | 31 +- probts/data/multi_horizon_dataset.py | 388 ++++ probts/model/forecaster/__init__.py | 3 +- probts/model/forecaster/forecaster.py | 32 +- .../forecaster/point_forecaster/__init__.py | 6 +- .../forecaster/point_forecaster/autoformer.py | 31 +- .../forecaster/point_forecaster/dlinear.py | 6 +- .../forecaster/point_forecaster/elastst.py | 194 ++ .../point_forecaster/forecastpfn.py | 167 -- .../model/forecaster/point_forecaster/gru.py | 6 + .../point_forecaster/itransformer.py | 6 + .../forecaster/point_forecaster/linear.py | 6 + .../model/forecaster/point_forecaster/mean.py | 33 - .../forecaster/point_forecaster/nhits.py | 6 + .../forecaster/point_forecaster/nlinear.py | 6 + .../forecaster/point_forecaster/patchtst.py | 16 +- .../forecaster/point_forecaster/timer.py | 93 - .../forecaster/point_forecaster/timesfm.py | 24 +- .../forecaster/point_forecaster/timesnet.py | 163 -- .../point_forecaster/tinytimemixer.py | 48 - .../point_forecaster/transformer.py | 8 +- .../point_forecaster/transformer_enc.py | 107 + .../forecaster/point_forecaster/tsmixer.py | 151 ++ .../forecaster/point_forecaster/units.py | 1087 ---------- .../forecaster/prob_forecaster/__init__.py | 40 - .../forecaster/prob_forecaster/chronos.py | 65 - .../model/forecaster/prob_forecaster/csdi.py | 200 -- .../forecaster/prob_forecaster/gru_maf.py | 114 - .../forecaster/prob_forecaster/gru_nvp.py | 114 - .../forecaster/prob_forecaster/lag_llama.py | 86 - .../forecaster/prob_forecaster/moirai.py | 62 - .../forecaster/prob_forecaster/timegrad.py | 112 - .../forecaster/prob_forecaster/trans_maf.py | 138 -- .../forecaster/prob_forecaster/tsdiff.py | 242 --- probts/model/nn/MAF.py | 197 -- probts/model/nn/RealNVP.py | 115 -- probts/model/nn/__init__.py | 3 - probts/model/nn/flow_model.py | 149 -- probts/model/nn/gaussian_diffusion.py | 424 ---- .../{ => AutoformerModule}/AutoCorrelation.py | 0 .../Autoformer_EncDec.py | 0 probts/model/nn/layers/Conv_Blocks.py | 60 - .../layers/ElasTSTModule/ElasTST_backbone.py | 282 +++ .../model/nn/layers/ElasTSTModule/Layers.py | 100 + .../model/nn/layers/ElasTSTModule/Modules.py | 121 ++ .../nn/layers/ElasTSTModule/SubLayers.py | 78 + probts/model/nn/layers/ElasTSTModule/TRoPE.py | 105 + .../model/nn/layers/ElasTSTModule/__init__.py | 0 probts/model/nn/layers/Moirai_backbone.py | 513 ----- .../{ => PatchTSTModule}/PatchTST_backbone.py | 2 +- .../{ => PatchTSTModule}/PatchTST_layers.py | 0 probts/model/nn/layers/TSMixer_layers.py | 395 ++++ probts/model/nn/layers/__init__.py | 6 +- probts/model/nn/layers/diffusion_layers.py | 173 -- probts/model/nn/layers/s4.py | 1840 ----------------- probts/model/nn/layers/s4_backbones.py | 184 -- probts/model/reweight_forecast_module.py | 180 ++ probts/utils/evaluator.py | 22 +- probts/utils/metrics.py | 2 +- probts/utils/position_emb.py | 59 + probts/utils/save_utils.py | 155 ++ probts/utils/utils.py | 211 +- run.py | 122 +- run.sh | 47 +- scripts/prepare_tsfm_checkpoints.sh | 28 - scripts/reproduce_ltsf_results.sh | 42 - scripts/reproduce_stsf_results.sh | 17 - scripts/reproduce_tsfm_results.sh | 148 -- scripts/run_ablation.sh | 27 + scripts/run_baselines.sh | 26 + 274 files changed, 4773 insertions(+), 12601 deletions(-) delete mode 100644 checkpoints/.gitignore delete mode 100644 checkpoints/README.md create mode 100644 config/ablation_study/elastst_abs_pe.yaml create mode 100644 config/ablation_study/elastst_learnable_pe.yaml create mode 100644 config/ablation_study/elastst_random_sample.yaml create mode 100644 config/ablation_study/elastst_trope.yaml create mode 100644 config/ablation_study/elastst_wo_mask.yaml create mode 100644 config/ablation_study/elastst_wo_patch.yaml create mode 100644 config/ablation_study/elastst_wo_pe.yaml create mode 100644 config/ablation_study/elastst_wo_trope.yaml rename config/{default => baselines/electricity_ltsf}/autoformer.yaml (82%) rename config/{ltsf => baselines}/electricity_ltsf/dlinear.yaml (100%) create mode 100644 config/baselines/electricity_ltsf/elastst.yaml rename config/{ltsf => baselines}/electricity_ltsf/patchtst.yaml (100%) create mode 100644 config/baselines/electricity_ltsf/transformer_enc.yaml create mode 100644 config/baselines/electricity_ltsf/tsmixer.yaml rename config/{stsf/electricity/dlinear.yaml => baselines/etth1/autoformer.yaml} (54%) rename config/{ltsf => baselines}/etth1/dlinear.yaml (100%) create mode 100644 config/baselines/etth1/elastst.yaml rename config/{ltsf => baselines}/etth1/patchtst.yaml (100%) rename config/{default/itransformer.yaml => baselines/etth1/transformer_enc.yaml} (79%) rename config/{stsf/solar/timesnet.yaml => baselines/etth1/tsmixer.yaml} (68%) create mode 100644 config/baselines/etth2/autoformer.yaml rename config/{ltsf => baselines}/etth2/dlinear.yaml (100%) create mode 100644 config/baselines/etth2/elastst.yaml rename config/{ltsf => baselines}/etth2/patchtst.yaml (100%) rename config/{ltsf/illness_ltsf/dlinear.yaml => baselines/etth2/transformer_enc.yaml} (58%) rename config/{stsf/exchange/timesnet.yaml => baselines/etth2/tsmixer.yaml} (68%) create mode 100644 config/baselines/ettm1/autoformer.yaml rename config/{ltsf => baselines}/ettm1/dlinear.yaml (100%) create mode 100644 config/baselines/ettm1/elastst.yaml rename config/{ltsf => baselines}/ettm1/patchtst.yaml (100%) rename config/{stsf/traffic/transformer.yaml => baselines/ettm1/transformer_enc.yaml} (58%) rename config/{default/nlinear.yaml => baselines/ettm1/tsmixer.yaml} (65%) create mode 100644 config/baselines/ettm2/autoformer.yaml rename config/{ltsf => baselines}/ettm2/dlinear.yaml (100%) create mode 100644 config/baselines/ettm2/elastst.yaml rename config/{ltsf => baselines}/ettm2/patchtst.yaml (100%) create mode 100644 config/baselines/ettm2/transformer_enc.yaml rename config/{stsf/electricity/timesnet.yaml => baselines/ettm2/tsmixer.yaml} (65%) create mode 100644 config/baselines/exchange_ltsf/autoformer.yaml rename config/{ltsf => baselines}/exchange_ltsf/dlinear.yaml (100%) create mode 100644 config/baselines/exchange_ltsf/elastst.yaml rename config/{ltsf => baselines}/exchange_ltsf/patchtst.yaml (100%) create mode 100644 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JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore diff --git a/README.md b/README.md index 42ee3af..318b78a 100644 --- a/README.md +++ b/README.md @@ -1,252 +1,153 @@ -
+# ElasTST (NeurIPS 2024) -# ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons +This is an offical implementation of ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer. ----------- -[ [Paper](https://arxiv.org/abs/2310.07446) | [Benchmarking](./docs/benchmark/README.md) | [Documentation](./docs/documentation/README.md) ] -A wide range of industrial applications desire precise point and distributional forecasting for diverse prediction horizons. ProbTS serves as a benchmarking tool to aid in understanding how advanced time-series models fulfill these essential forecasting needs. It also sheds light on their advantages and disadvantages in addressing different challenges and unveil the possibilities for future research. +## Key Designs 💡 -To achieve these objectives, ProbTS provides a unified pipeline that implements [cutting-edge models](#-available-models) from different research threads, including: -- Long-term point forecasting approaches, such as [PatchTST](https://arxiv.org/abs/2211.14730), [iTransformer](https://arxiv.org/abs/2310.06625), etc. -- Short-term probabilistic forecasting methods, such as [TimeGrad](https://arxiv.org/abs/2101.12072), [CSDI](https://arxiv.org/abs/2107.03502), etc. -- Recent time-series foundation models for universal forecasting, such as [TimesFM](https://arxiv.org/abs/2310.10688), [MOIRAI](https://arxiv.org/abs/2402.02592), etc. +
-Specifically, ProbTS emphasizes the differences in their primary methodological designs, including: -- Supporting point or distributional forecasts -- Using autoregressive or non-autoregressive decoding schemes for multi-step outputs +ElasTST (Elastic Time-Series Transformer) is crafted to deliver robust, consistent forecasts across varied horizons. Its design leverages several unique features to ensure adaptability and accuracy: -
+(a) **Structured Attention Masks**: Control the flow of information by allowing placeholders to focus solely on observed time-series patches, preserving the model's horizon-invariant capability. -## Available Models 🧩 +(b) **Tunable Rotary Position Embedding (RoPE)**: Capture customized period patterns, enabling seamless adaptation to different forecasting horizons. -ProbTS includes both classical time-series models, specializing in long-term point forecasting or short-term distributional forecasting, and recent time-series foundation models that offer zero-shot and arbitrary-horizon forecasting capabilities for new time series. - -### Classical Time-series Models - -| **Model** | **Original Eval. Horizon** | **Estimation** | **Decoding Scheme** | **Class Path** | -| --- | --- | --- | --- | --- | -| Linear | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.LinearForecaster` | -| [GRU](https://arxiv.org/abs/1412.3555) | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.GRUForecaster` | -| [Transformer](https://arxiv.org/abs/1706.03762) | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.TransformerForecaster` | -| [Autoformer](https://arxiv.org/abs/2106.13008) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.Autoformer` | -| [N-HiTS](https://arxiv.org/abs/2201.12886) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.NHiTS` | -| [NLinear](https://arxiv.org/abs/2205.13504) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.NLinear` | -| [DLinear](https://arxiv.org/abs/2205.13504) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.DLinear` | -| [TimesNet](https://arxiv.org/abs/2210.02186) | Short- / Long-term | Point | Non-auto | `probts.model.forecaster.point_forecaster.TimesNet` | -| [PatchTST](https://arxiv.org/abs/2211.14730) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.PatchTST` | -| [iTransformer](https://arxiv.org/abs/2310.06625) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.iTransformer` | -| [GRU NVP](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.GRU_NVP` | -| [GRU MAF](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.GRU_MAF` | -| [Trans MAF](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.Trans_MAF` | -| [TimeGrad](https://arxiv.org/abs/2101.12072) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.TimeGrad` | -| [CSDI](https://arxiv.org/abs/2107.03502) | Short-term | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.CSDI` | -| [TSDiff](https://arxiv.org/abs/2307.11494) | Short-term | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.TSDiffCond` | - -### Fundation Models - -| **Model** | **Any Horizon** | **Estimation** | **Decoding Scheme** | **Class Path** | -| --- | --- | --- | --- | --- | -| [Lag-Llama](https://arxiv.org/abs/2310.08278) | ✔ | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.LagLlama` | -| [ForecastPFN](https://arxiv.org/abs/2311.01933) | ✔ | Point | Non-auto | `probts.model.forecaster.point_forecaster.ForecastPFN` | -| [TimesFM](https://arxiv.org/abs/2310.10688) | ✔ | Point | Auto | `probts.model.forecaster.point_forecaster.TimesFM` | -| [TTM](https://arxiv.org/abs/2401.03955) | ✘ | Point | Non-auto | `probts.model.forecaster.point_forecaster.TinyTimeMixer` | -| [Timer](https://arxiv.org/abs/2402.02368) | ✔ | Point | Auto | `probts.model.forecaster.point_forecaster.Timer` | -| [MOIRAI](https://arxiv.org/abs/2402.02592) | ✔ | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.Moirai` | -| [UniTS](https://arxiv.org/abs/2403.00131) | ✔ | Point | Non-auto | `probts.model.forecaster.point_forecaster.UniTS` | -| [Chronos](https://arxiv.org/abs/2403.07815) | ✔ | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.Chronos` | - -Stay tuned for more models to be added in the future. +(c) **Multi-Scale Patch Assembly**: Integrate fine-grained and coarse-grained information, balancing precision for short-term and long-term forecasting. +(d) **Training Horizon Reweighting**: Simulate the effects of multi-horizon sampling with a single fixed horizon, streamlining training without compromising robustness. ## Setup :wrench: -### Environment +### Installation -ProbTS is developed with Python 3.10 and relies on [PyTorch Lightning](https://github.com/Lightning-AI/lightning). To set up the environment: +ElasTST is developed with Python 3.10 and relies on [PyTorch Lightning](https://github.com/Lightning-AI/lightning). -```bash -# Create a new conda environment -conda create -n probts python=3.10 -conda activate probts +1. Clone repository: -# Install required packages -pip install . -pip uninstall -y probts # recommended to uninstall the root package (optional) -``` + ```bash + # Clone ProbTS repo + git clone https://github.com/microsoft/ProbTS.git + cd ProbTS -[Optional] For time-series foundation models, you need to install basic packages and additional dependencies: + # Switch to ElasTST branch + git pull origin elastst:elastst + git checkout elastst + ``` -```bash -# Create a new conda environment -conda create -n probts_fm python=3.10 -conda activate probts_fm +2. Set up the environment: -# Git submodule -git submodule update --init --recursive + ```bash + # Create a new conda environment + conda create -n elastst python=3.10 + conda activate elastst -# Install additional packages for foundation models -pip install ".[tsfm]" -pip uninstall -y probts # recommended to uninstall the root package (optional) + # Install required packages + pip install . + ``` -# For MOIRAI, we fix the version of the package for better performance -cd submodules/uni2ts -git reset --hard fce6a6f57bc3bc1a57c7feb3abc6c7eb2f264301 -``` -
- -Optional for TSFMs reproducibility +### Download Datasets +To download the [datasets used in our paper](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy), please run: ```bash -# For TimesFM, fix the version for reproducibility (optional) -cd submodules/timesfm -git reset --hard 5c7b905 - -# For Lag-Llama, fix the version for reproducibility (optional) -cd submodules/lag_llama -git reset --hard 4ad82d9 - -# For TinyTimeMixer, fix the version for reproducibility (optional) -cd submodules/tsfm -git reset --hard bb125c14a05e4231636d6b64f8951d5fe96da1dc +bash scripts/prepare_datasets.sh "./datasets" ``` -
- -### Datasets - -- **Short-Term Forecasting**: We use datasets from [GluonTS](https://github.com/awslabs/gluonts). - Configure the datasets using `--data.data_manager.init_args.dataset {DATASET_NAME}`. You can choose from multivariate or univariate datasets as per your requirement. - ```bash - # Multivariate Datasets - ['exchange_rate_nips', 'electricity_nips', 'traffic_nips', 'solar_nips', 'wiki2000_nips'] - - # Univariate Datasets - ['tourism_monthly', 'tourism_quarterly', 'tourism_yearly', 'm4_hourly', 'm4_daily', 'm4_weekly', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5'] - ``` - -- **Long-Term Forecasting**: To download the [long-term forecasting datasets](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy), please follow these steps: - ```bash - bash scripts/prepare_datasets.sh "./datasets" - ``` - - Configure the datasets using `--data.data_manager.init_args.dataset {DATASET_NAME}` with the following list of available datasets: - ```bash - # Long-term Forecasting - ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool'] - ``` - Note: When utilizing long-term forecasting datasets, you must explicitly specify the `context_length` and `prediction_length` parameters. For example, to set a context length of 96 and a prediction length of 192, use the following command-line arguments: - ```bash - --data.data_manager.init_args.context_length 96 \ - --data.data_manager.init_args.prediction_length 192 \ - ``` - -- **Using Datasets from Monash Time Series Forecasting Repository**: To use datasets from the [Monash Time Series Forecasting Repository](https://forecastingdata.org/), follow these steps: - - 1. **Download the Dataset**: - - Navigate to the target dataset, such as the [Electricity Hourly Dataset](https://zenodo.org/records/4656140). - - Download the `.tsf` file and place it in your local `datasets` directory (e.g., `./datasets`). - - 2. **Configure the Dataset**: - - Use the following configuration to specify the dataset, file path, and frequency: - ```bash - --data.data_manager.init_args.dataset {DATASET_NAME} \ - --data.data_manager.init_args.data_path /path/to/data_file.tsf \ - --data.data_manager.init_args.freq {FREQ} - ``` - - - **Example Configuration**: - ```bash - --data.data_manager.init_args.dataset monash_electricity_hourly \ - --data.data_manager.init_args.data_path ./datasets/electricity_hourly_dataset.tsf \ - --data.data_manager.init_args.freq H \ - --data.data_manager.init_args.context_length 96 \ - --data.data_manager.init_args.prediction_length 96 \ - --data.data_manager.init_args.multivariate true - ``` - - Note 1: Refer to the [Pandas Time Series Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) for the correct frequency values (`{FREQ}`) to use in your configuration. - Note 2: You can adjust the test instance sampling using the `--data.data_manager.init_args.test_rolling_length` parameter. The default value is `96`. - -### Checkpoints for Foundation Models +## Quick Start :rocket: -Download the checkpoints with the following command (details can be found [here](./checkpoints/README.md)): -```bash -bash scripts/prepare_tsfm_checkpoints.sh # By downloading, you agree to the original licenses -``` -## Quick Start :rocket: +### Running the Model -Specify `--config` with a specific configuration file to reproduce results of point or probabilistic models on commonly used long- and short-term forecasting datasets. Configuration files are included in the [config](./config/) folder. +To run ElasTST, simply execute: -To run models: ```bash bash run.sh ``` -Experimental results reproduction: - -- **Long-term Forecasting:** - - ```bash - bash scripts/reproduce_ltsf_results.sh - ``` +You can adjust the configuration as needed: +```bash +python run.py --config config/elastst.yaml \ + --data.data_manager.init_args.path ./datasets \ + --trainer.default_root_dir /path/to/log_dir/ \ + --data.data_manager.init_args.dataset {DATASET_NAME} \ + --data.data_manager.init_args.context_length ${CTX_LEN} \ + --data.data_manager.init_args.prediction_length ${TEST_PRED_LEN} \ + --data.data_manager.init_args.train_pred_len_list ${TRAIN_PRED_LEN} \ + --data.data_manager.init_args.train_ctx_len_list ${TRAIN_CTX_LEN} \ + --data.data_manager.init_args.val_pred_len_list ${VAL_PRED_LEN} \ -- **Short-term Forecasting:** +``` +- `DATASET_NAME`: Select from ```bash - bash scripts/reproduce_stsf_results.sh + ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'weather_ltsf'] ``` +- `CTX_LEN`: Context length in the validation and testing phase. +- `TRAIN_CTX_LEN`: Context length in the training phase. +- `TEST_PRED_LEN`: Forecasting horizons in the testing phase. +- `VAL_PRED_LEN`: Forecasting horizons for performance validation. +- `TRAIN_PRED_LEN`: Forecasting horizons in the training phase. -- **Time Series Foundation Models:** - ```bash - bash scripts/reproduce_tsfm_results.sh - ``` +### Reproducing Results -### Short-term Forecasting Configuration - -For short-term forecasting scenarios, datasets and corresponding `context_length` and `prediction_length` are automatically obtained from [GluonTS](https://github.com/awslabs/gluonts). Use the following command: +To reproduce results, use the specific config file from the [config](./config/) folder. +For baseline reproduction: ```bash -python run.py --config config/path/to/model.yaml \ - --data.data_manager.init_args.path /path/to/datasets/ \ - --trainer.default_root_dir /path/to/log_dir/ \ - --data.data_manager.init_args.dataset {DATASET_NAME} +bash scripts/run_baselines.sh ``` -See full `DATASET_NAME` list: -```python -from gluonts.dataset.repository import dataset_names -print(dataset_names) -``` - -### Long-term Forecasting Configuration -For long-term forecasting scenarios, `context_length` and `prediction_length` must be explicitly assigned: - -```bash -python run.py --config config/path/to/model.yaml \ - --data.data_manager.init_args.path /path/to/datasets/ \ - --trainer.default_root_dir /path/to/log_dir/ \ - --data.data_manager.init_args.dataset {DATASET_NAME} \ - --data.data_manager.init_args.context_length {CTX_LEN} \ - --data.data_manager.init_args.prediction_length {PRED_LEN} -``` +For ablation studies: -`DATASET_NAME` options: ```bash -['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool'] +bash scripts/run_ablation.sh ``` -## Benchmarking :balance_scale: - -By utilizing ProbTS, we conduct a systematic comparison between studies that focus on point forecasting and those aimed at distributional estimation, employing various forecasting horizons and evaluation metrics. For more details -- [Short-term & Long-term Forecasting Benchmarking](./docs/benchmark/README.md) -- [Evaluating Time Series Foundation Models](./docs/benchmark/FOUNDATION_MODEL.md) +## Key Configuration Parameters for ElasTST + + +| **Config Name** | **Type** | **Description** | +|-------------------------------------------|--------------------------------------|-----------------| +| `data.data_manager.init_args.dataset` | `str` | Dataset for training and evaluation. | +| `data.data_manager.init_args.context_length` | `Union[str, int, list]` | Length of observation window in inference phase. | +| `data.data_manager.init_args.prediction_length` | `Union[str, int, list]` | Forecasting horizon length in inference phase. | +| `data.data_manager.init_args.val_pred_len_list` | `Union[str, int, list]` | Forecasting horizon length for performance validation. | +| `data.data_manager.init_args.train_pred_len_list`| `Union[str, int, list]` | Length of observation window in training phase. | +| `data.data_manager.init_args.train_ctx_len_list` | `Union[str, int, list]` | Forecasting horizons in training phase. | +| `model.forecaster.init_args.l_patch_size` | `Union[str, int, list]` | Patch sizes configuration. | +| `model.forecaster.init_args.k_patch_size` | `int` | Patch size for variables. | +| `model.forecaster.init_args.stride` | `int` | Stride for patch splitting. If None, uses patch size as default. | +| `model.forecaster.init_args.rotate` | `bool` | Apply rotational positional embeddings. | +| `model.forecaster.init_args.addv` | `bool` | Whether to add RoPE information to value in attention. If False, only rotate the key and query embeddings. | +| `model.forecaster.init_args.bin_att` | `bool` | Use binary attention biases to encode variate indices (any-variate attention). | +| `model.forecaster.rope_theta_init` | `str` | Initialization for TRoPE. Options: ['exp', 'linear', 'uniform', 'rope']. | +| `model.forecaster.min_period` | `float` | Minimum initialized period coefficient for rotary embeddings. | +| `model.forecaster.max_period` | `float` | Maximum initialized period coefficient for rotary embeddings. | +| `model.forecaster.learn_tem_emb` | `bool` | Whether to use learnable temporal embeddings. | +| `model.forecaster.learnable_rope` | `bool` | Make period coefficient in TRoPE learnable. | +| `model.forecaster.abs_tem_emb` | `bool` | Use absolute temporal embeddings if True. | +| `model.forecaster.structured_mask` | `bool` | Apply structured mask or not. | +| `model.forecaster.max_seq_len` | `int` | Maximum sequence length for the input time series. | +| `model.forecaster.theta_base` | `int` | Base frequency of vanilla RoPE. | +| `model.forecaster.t_layers` | `int` | Number of temporal attention layers. | +| `model.forecaster.v_layers` | `int` | Number of variable attention layers. | +| `model.forecaster.patch_share_backbone` | `bool` | Share Transformer backbone across patches. | +| `model.forecaster.n_heads` | `int` | Number of attention heads in the multi-head attention mechanism. | +| `model.forecaster.d_k` | `int` | Dimensionality of key embeddings in attention. | +| `model.forecaster.d_v` | `int` | Dimensionality of value embeddings in attention. | +| `model.forecaster.d_inner` | `int` | Size of inner layers in the feed-forward network. | +| `model.forecaster.dropout` | `float` | Dropout rate for regularization during training. | +| `model.forecaster.in_channels` | `int` | Number of input channels in the time series data. We only consider univariable. | +| `model.forecaster.f_hidden_size` | `int` | Hidden size for the feed-forward layers. | +| `model.forecaster.use_norm` | `bool` | Whether to apply instance normalization. | +| `model.forecaster.use_norm` | `bool` | Whether to apply instance normalization. | +| `model.sampling_weight_scheme` | `str` | The scheme of training horizon reweighting. Options: ['random', 'none', 'const'].| +| `data.data_manager.init_args.continous_sample` | `bool` | If True, sampling horizons from `[min(train_pred_len_list), max(train_pred_len_list)]`, else sampling within the set `train_pred_len_list`. See `./config/ablation_study/elastst_random_sample.yaml` for details.| ## Documentation :open_book: @@ -254,78 +155,14 @@ By utilizing ProbTS, we conduct a systematic comparison between studies that foc For detailed information on configuration parameters and model customization, please refer to the [documentation](./docs/documentation/README.md). -### Key Configuration Parameters - -- Adjust model and data parameters in `run.sh`. Key parameters include: - -| Config Name | Type | Description | -| --- | --- | --- | -| `trainer.max_epochs` | integer | Maximum number of training epochs. | -| `model.forecaster.class_path` | string | Forecaster module path (e.g., `probts.model.forecaster.point_forecaster.PatchTST`). | -| `model.forecaster.init_args.{ARG}` | - | Model-specific hyperparameters. | -| `model.num_samples` | integer | Number of samples per distribution during evaluation. | -| `model.learning_rate` | float | Learning rate. | -| `data.data_manager.init_args.dataset` | string | Dataset for training and evaluation. | -| `data.data_manager.init_args.path` | string | Path to the dataset folder. | -| `data.data_manager.init_args.scaler` | string | Scaler type: `identity`, `standard` (z-score normalization), or `temporal` (scale based on average temporal absolute value). | -| `data.data_manager.init_args.context_length` | integer | Length of observation window (required for long-term forecasting). | -| `data.data_manager.init_args.prediction_length` | integer | Forecasting horizon length (required for long-term forecasting). | -| `data.data_manager.init_args.var_specific_norm` | boolean | If conduct per-variate normalization or not. | -| `data.batch_size` | integer | Batch size. | - - -- To print the full pipeline configuration to a file: - - ```bash - python run.py --print_config > config/pipeline_config.yaml - ``` - -## Acknowledgement 🌟 - -Special thanks to the following repositories for their open-sourced code bases and datasets. - -### Tools/Packages - -- [GluonTS](https://github.com/awslabs/gluonts) -- [PyTorch-TS](https://github.com/zalandoresearch/pytorch-ts) -- [TSLib](https://github.com/libts/tslib) -- [NeuralForecast](https://github.com/Nixtla/neuralforecast) - -### Official Implementations - -**Classical Time-series Models** - -- [Autoformer](https://github.com/thuml/Autoformer) -- [N-HiTS](https://github.com/cchallu/n-hits) -- [NLinear, DLinear](https://github.com/cure-lab/LTSF-Linear) -- [TimesNet](https://github.com/thuml/Time-Series-Library) -- [RevIN](https://github.com/ts-kim/RevIN) -- [PatchTST](https://github.com/yuqinie98/PatchTST) -- [iTransformer](https://github.com/thuml/iTransformer) -- [GRU NVP, GRU MAF, Trans MAF, TimeGrad](https://github.com/zalandoresearch/pytorch-ts/tree/master) -- [CSDI](https://github.com/ermongroup/CSDI) -- [TSDiff](https://github.com/amazon-science/unconditional-time-series-diffusion) - - -**Time-series Foundation Models** - -- [MOIRAI](https://github.com/SalesforceAIResearch/uni2ts) -- [Chronos](https://github.com/amazon-science/chronos-forecasting) -- [Lag-Llama](https://github.com/time-series-foundation-models/lag-llama) -- [TimesFM](https://github.com/google-research/timesfm) -- [Timer](https://github.com/thuml/Large-Time-Series-Model) -- [UniTS](https://github.com/mims-harvard/UniTS) -- [ForecastPFN](https://github.com/abacusai/ForecastPFN) -- [TTM](https://github.com/ibm-granite/granite-tsfm) - -## Citing ProbTS 🌟 +## Citing ElasTST 🌟 -If you have used ProbTS for research or production, please cite it as follows. +If you find ElasTST useful in your research, please cite: ```tex -@article{zhang2023probts, - title={{ProbTS}: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons}, - author={Zhang, Jiawen and Wen, Xumeng and Zhang, Zhenwei and Zheng, Shun and Li, Jia and Bian, Jiang}, - journal={arXiv preprint arXiv:2310.07446}, - year={2023} +@inproceedings{zhang2024elastst, + title={ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer}, + author={Jiawen Zhang and Shun Zheng and Wen Xumeng and Xiaofang Zhou and Jiang Bian and Li Jia}, + booktitle={Advances in Neural Information Processing Systems}, + year={2024} } ``` \ No newline at end of file diff --git a/checkpoints/.gitignore b/checkpoints/.gitignore deleted file mode 100644 index b085fca..0000000 --- a/checkpoints/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -# Ignore all files -* - -# Except README.md -!README.md \ No newline at end of file diff --git a/checkpoints/README.md b/checkpoints/README.md deleted file mode 100644 index 89f008d..0000000 --- a/checkpoints/README.md +++ /dev/null @@ -1,30 +0,0 @@ -# Checkpoints for Foundation Models - -For full reproducibility, we provide the checkpoints for some foundation models as of the paper completion date. - -Download the checkpoints from [Google Drive](https://drive.google.com/drive/folders/1FaCk9Lj9KZGEO09gehNqC4fbTj4wnN8j?usp=sharing) with: - -```bash -# By downloading, you agree to the terms of the original license agreements. -sh scripts/prepare_checkpoints.sh # in root directory -``` - - -You can also download the newest checkpoints from the following repositories: - -- For `Timer`, download the checkpoints from its [official repository](https://github.com/thuml/Large-Time-Series-Model?tab=readme-ov-file#code-for-fine-tuning) ([Google Drive](https://drive.google.com/drive/folders/15oaiAl4OO5gFqZMJD2lOtX2fxHbpgcU8) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/235e6bfcf5fa440bb119/)) under the folder `./checkpoints/timer/Timer_67M_UTSD_4G.pt`. -- For `ForecastPFN`, download the checkpoints from its [official repository](https://github.com/abacusai/ForecastPFN#installation-) ([Google Drive](https://drive.google.com/file/d/1acp5thS7I4g_6Gw40wNFGnU1Sx14z0cU/view)) under the folder `./checkpoints/ForecastPFN/saved_weights`. -- For `UniTS`, download the checkpoints `units_x128_pretrain_checkpoint.pth` from its [official repository](https://github.com/mims-harvard/UniTS/releases/tag/ckpt) under the folder `./checkpoints/units/units_x128_pretrain_checkpoint.pth`. -- For `Lag-Llama`, download the checkpoints `lag-llama.ckpt` from its [huggingface repository](https://huggingface.co/time-series-foundation-models/Lag-Llama/tree/main) under the folder `./checkpoints/lag-llama/lag-llama.ckpt`. -- For other models, they can be automatically downloaded from huggingface during the first run. - -
- -| **Model** | **HuggingFace** | -| --- | --- | -| `MOIRAI` | [Link](https://huggingface.co/Salesforce/moirai-1.0-R-small) | -| `Chronos` | [Link](https://huggingface.co/amazon/chronos-t5-large) | -| `TinyTimeMixer` | [Link](https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1) | -| `TimesFM` | [Link](https://huggingface.co/google/timesfm-1.0-200m) | - -
diff --git a/config/ablation_study/elastst_abs_pe.yaml b/config/ablation_study/elastst_abs_pe.yaml new file mode 100644 index 0000000..e1cd908 --- /dev/null +++ b/config/ablation_study/elastst_abs_pe.yaml @@ -0,0 +1,54 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: false + rope_theta_init: 'exp' + learnable_rope: false + abs_tem_emb: true + learn_tem_emb: false + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_learnable_pe.yaml b/config/ablation_study/elastst_learnable_pe.yaml new file mode 100644 index 0000000..a933c67 --- /dev/null +++ b/config/ablation_study/elastst_learnable_pe.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: false + rope_theta_init: 'exp' + learnable_rope: false + abs_tem_emb: false + learn_tem_emb: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_random_sample.yaml b/config/ablation_study/elastst_random_sample.yaml new file mode 100644 index 0000000..099dc87 --- /dev/null +++ b/config/ablation_study/elastst_random_sample.yaml @@ -0,0 +1,55 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 1-720 # the horizon sampling interval + val_pred_len_list: 96-192-336-720 + # if continous_sample is set to true, sampling horizons from [min(train_pred_len_list), max(train_pred_len_list)], + # if continous_sample is set to false, sampling horizons from train_pred_len_list, + continous_sample: true + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_trope.yaml b/config/ablation_study/elastst_trope.yaml new file mode 100644 index 0000000..33cd176 --- /dev/null +++ b/config/ablation_study/elastst_trope.yaml @@ -0,0 +1,52 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_wo_mask.yaml b/config/ablation_study/elastst_wo_mask.yaml new file mode 100644 index 0000000..d7ef768 --- /dev/null +++ b/config/ablation_study/elastst_wo_mask.yaml @@ -0,0 +1,52 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: false + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_wo_patch.yaml b/config/ablation_study/elastst_wo_patch.yaml new file mode 100644 index 0000000..5b7d41c --- /dev/null +++ b/config/ablation_study/elastst_wo_patch.yaml @@ -0,0 +1,52 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '16' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_wo_pe.yaml b/config/ablation_study/elastst_wo_pe.yaml new file mode 100644 index 0000000..888774f --- /dev/null +++ b/config/ablation_study/elastst_wo_pe.yaml @@ -0,0 +1,52 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: false + rope_theta_init: 'exp' + learnable_rope: false + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ablation_study/elastst_wo_trope.yaml b/config/ablation_study/elastst_wo_trope.yaml new file mode 100644 index 0000000..28121d4 --- /dev/null +++ b/config/ablation_study/elastst_wo_trope.yaml @@ -0,0 +1,52 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'rope' + learnable_rope: false + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: none +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + train_ctx_len_list: 96 + prediction_length: 24-96-192-336-720-1024 + train_pred_len_list: 336 + val_pred_len_list: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/default/autoformer.yaml b/config/baselines/electricity_ltsf/autoformer.yaml similarity index 82% rename from config/default/autoformer.yaml rename to config/baselines/electricity_ltsf/autoformer.yaml index 25428ea..d624204 100644 --- a/config/default/autoformer.yaml +++ b/config/baselines/electricity_ltsf/autoformer.yaml @@ -10,8 +10,6 @@ trainer: log_every_n_steps: 1 default_root_dir: ./results accumulate_grad_batches: 1 - # num_sanity_val_steps: 0 - # gradient_clip_algorithm: 'norm' model: forecaster: class_path: probts.model.forecaster.point_forecaster.Autoformer @@ -23,8 +21,6 @@ model: e_layers: 2 d_layers: 1 output_attention: false - d_ff: 512 - f_hidden_size: 512 embed: 'timeF' use_lags: false use_feat_idx_emb: false @@ -37,9 +33,11 @@ data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: solar_nips + dataset: electricity_ltsf split_val: true - scaler: standard # none, standard, scaling + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 32 test_batch_size: 32 num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/electricity_ltsf/dlinear.yaml b/config/baselines/electricity_ltsf/dlinear.yaml similarity index 100% rename from config/ltsf/electricity_ltsf/dlinear.yaml rename to config/baselines/electricity_ltsf/dlinear.yaml diff --git a/config/baselines/electricity_ltsf/elastst.yaml b/config/baselines/electricity_ltsf/elastst.yaml new file mode 100644 index 0000000..b868795 --- /dev/null +++ b/config/baselines/electricity_ltsf/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 200 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 8 + test_batch_size: 8 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/electricity_ltsf/patchtst.yaml b/config/baselines/electricity_ltsf/patchtst.yaml similarity index 100% rename from config/ltsf/electricity_ltsf/patchtst.yaml rename to config/baselines/electricity_ltsf/patchtst.yaml diff --git a/config/baselines/electricity_ltsf/transformer_enc.yaml b/config/baselines/electricity_ltsf/transformer_enc.yaml new file mode 100644 index 0000000..c5e2181 --- /dev/null +++ b/config/baselines/electricity_ltsf/transformer_enc.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerEnc + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/baselines/electricity_ltsf/tsmixer.yaml b/config/baselines/electricity_ltsf/tsmixer.yaml new file mode 100644 index 0000000..3ece958 --- /dev/null +++ b/config/baselines/electricity_ltsf/tsmixer.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TSMixer + init_args: + num_blocks: 6 + dropout_rate: 0.7 + ff_dim: 64 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/dlinear.yaml b/config/baselines/etth1/autoformer.yaml similarity index 54% rename from config/stsf/electricity/dlinear.yaml rename to config/baselines/etth1/autoformer.yaml index 4e60590..99b034c 100644 --- a/config/stsf/electricity/dlinear.yaml +++ b/config/baselines/etth1/autoformer.yaml @@ -1,5 +1,5 @@ # lightning==2.3.0.dev0 -seed_everything: 1 +seed_everything: 0 trainer: accelerator: gpu devices: 1 @@ -9,24 +9,35 @@ trainer: limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear + class_path: probts.model.forecaster.point_forecaster.Autoformer init_args: - individual: true - kernel_size: 3 + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' use_lags: false use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.01 + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: electricity_nips + dataset: etth1 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 32 test_batch_size: 32 num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth1/dlinear.yaml b/config/baselines/etth1/dlinear.yaml similarity index 100% rename from config/ltsf/etth1/dlinear.yaml rename to config/baselines/etth1/dlinear.yaml diff --git a/config/baselines/etth1/elastst.yaml b/config/baselines/etth1/elastst.yaml new file mode 100644 index 0000000..ecb8c40 --- /dev/null +++ b/config/baselines/etth1/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 10 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 4 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth1/patchtst.yaml b/config/baselines/etth1/patchtst.yaml similarity index 100% rename from config/ltsf/etth1/patchtst.yaml rename to config/baselines/etth1/patchtst.yaml diff --git a/config/default/itransformer.yaml b/config/baselines/etth1/transformer_enc.yaml similarity index 79% rename from config/default/itransformer.yaml rename to config/baselines/etth1/transformer_enc.yaml index 0e2bb33..1840959 100644 --- a/config/default/itransformer.yaml +++ b/config/baselines/etth1/transformer_enc.yaml @@ -12,7 +12,7 @@ trainer: accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.iTransformer + class_path: probts.model.forecaster.point_forecaster.TransformerEnc init_args: factor: 1 n_heads: 8 @@ -33,9 +33,11 @@ data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: solar_nips + dataset: etth1 split_val: true - scaler: standard # none, standard, scaling + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 32 test_batch_size: 32 num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/timesnet.yaml b/config/baselines/etth1/tsmixer.yaml similarity index 68% rename from config/stsf/solar/timesnet.yaml rename to config/baselines/etth1/tsmixer.yaml index f23eabc..b44cc04 100644 --- a/config/stsf/solar/timesnet.yaml +++ b/config/baselines/etth1/tsmixer.yaml @@ -4,33 +4,34 @@ trainer: accelerator: gpu devices: 1 strategy: auto - max_epochs: 50 + max_epochs: 40 use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet + class_path: probts.model.forecaster.point_forecaster.TSMixer init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 16 - dropout: 0.1 - f_hidden_size: 16 + num_blocks: 4 + dropout_rate: 0.9 + ff_dim: 256 use_lags: false use_feat_idx_emb: false use_time_feat: false + feat_idx_emb_dim: 1 learning_rate: 0.001 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: solar_nips + dataset: etth1 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 64 test_batch_size: 64 num_workers: 8 diff --git a/config/baselines/etth2/autoformer.yaml b/config/baselines/etth2/autoformer.yaml new file mode 100644 index 0000000..8c3c6d2 --- /dev/null +++ b/config/baselines/etth2/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth2/dlinear.yaml b/config/baselines/etth2/dlinear.yaml similarity index 100% rename from config/ltsf/etth2/dlinear.yaml rename to config/baselines/etth2/dlinear.yaml diff --git a/config/baselines/etth2/elastst.yaml b/config/baselines/etth2/elastst.yaml new file mode 100644 index 0000000..884110a --- /dev/null +++ b/config/baselines/etth2/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 10 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth2/patchtst.yaml b/config/baselines/etth2/patchtst.yaml similarity index 100% rename from config/ltsf/etth2/patchtst.yaml rename to config/baselines/etth2/patchtst.yaml diff --git a/config/ltsf/illness_ltsf/dlinear.yaml b/config/baselines/etth2/transformer_enc.yaml similarity index 58% rename from config/ltsf/illness_ltsf/dlinear.yaml rename to config/baselines/etth2/transformer_enc.yaml index a1d52f1..5fa10de 100644 --- a/config/ltsf/illness_ltsf/dlinear.yaml +++ b/config/baselines/etth2/transformer_enc.yaml @@ -1,5 +1,5 @@ # lightning==2.3.0.dev0 -seed_everything: 1 +seed_everything: 0 trainer: accelerator: gpu devices: 1 @@ -8,28 +8,36 @@ trainer: use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 - accumulate_grad_batches: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear + class_path: probts.model.forecaster.point_forecaster.TransformerEnc init_args: - individual: false - kernel_size: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 use_lags: false use_feat_idx_emb: false use_time_feat: false - learning_rate: 0.01 + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: illness_ltsf + dataset: etth2 split_val: true scaler: standard # identity, standard, temporal - context_length: 36 - prediction_length: 36 + context_length: 96 + prediction_length: 96 batch_size: 32 test_batch_size: 32 - num_workers: 8 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/timesnet.yaml b/config/baselines/etth2/tsmixer.yaml similarity index 68% rename from config/stsf/exchange/timesnet.yaml rename to config/baselines/etth2/tsmixer.yaml index 2e7838c..d9439d7 100644 --- a/config/stsf/exchange/timesnet.yaml +++ b/config/baselines/etth2/tsmixer.yaml @@ -4,33 +4,34 @@ trainer: accelerator: gpu devices: 1 strategy: auto - max_epochs: 50 + max_epochs: 40 use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet + class_path: probts.model.forecaster.point_forecaster.TSMixer init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 64 - dropout: 0.1 - f_hidden_size: 64 + num_blocks: 1 + dropout_rate: 0.9 + ff_dim: 16 use_lags: false use_feat_idx_emb: false use_time_feat: false + feat_idx_emb_dim: 1 learning_rate: 0.0001 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: exchange_rate_nips + dataset: etth2 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 64 test_batch_size: 64 num_workers: 8 diff --git a/config/baselines/ettm1/autoformer.yaml b/config/baselines/ettm1/autoformer.yaml new file mode 100644 index 0000000..2704d16 --- /dev/null +++ b/config/baselines/ettm1/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm1/dlinear.yaml b/config/baselines/ettm1/dlinear.yaml similarity index 100% rename from config/ltsf/ettm1/dlinear.yaml rename to config/baselines/ettm1/dlinear.yaml diff --git a/config/baselines/ettm1/elastst.yaml b/config/baselines/ettm1/elastst.yaml new file mode 100644 index 0000000..c36ea7d --- /dev/null +++ b/config/baselines/ettm1/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 10 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm1/patchtst.yaml b/config/baselines/ettm1/patchtst.yaml similarity index 100% rename from config/ltsf/ettm1/patchtst.yaml rename to config/baselines/ettm1/patchtst.yaml diff --git a/config/stsf/traffic/transformer.yaml b/config/baselines/ettm1/transformer_enc.yaml similarity index 58% rename from config/stsf/traffic/transformer.yaml rename to config/baselines/ettm1/transformer_enc.yaml index cf89ac0..ed49552 100644 --- a/config/stsf/traffic/transformer.yaml +++ b/config/baselines/ettm1/transformer_enc.yaml @@ -1,5 +1,5 @@ # lightning==2.3.0.dev0 -seed_everything: 1 +seed_everything: 0 trainer: accelerator: gpu devices: 1 @@ -9,30 +9,35 @@ trainer: limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + class_path: probts.model.forecaster.point_forecaster.TransformerEnc init_args: - f_hidden_size: 32 - num_heads: 8 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false feat_idx_emb_dim: 1 - learning_rate: 0.001 + num_samples: 1 + learning_rate: 1e-4 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: traffic_nips + dataset: ettm1 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 32 test_batch_size: 32 num_workers: 8 \ No newline at end of file diff --git a/config/default/nlinear.yaml b/config/baselines/ettm1/tsmixer.yaml similarity index 65% rename from config/default/nlinear.yaml rename to config/baselines/ettm1/tsmixer.yaml index b9edac8..4756ec2 100644 --- a/config/default/nlinear.yaml +++ b/config/baselines/ettm1/tsmixer.yaml @@ -4,28 +4,34 @@ trainer: accelerator: gpu devices: 1 strategy: auto - max_epochs: 50 + max_epochs: 40 use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.NLinear + class_path: probts.model.forecaster.point_forecaster.TSMixer init_args: - individual: false + num_blocks: 4 + dropout_rate: 0.9 + ff_dim: 16 use_lags: false use_feat_idx_emb: false use_time_feat: false - learning_rate: 0.01 + feat_idx_emb_dim: 1 + learning_rate: 0.0001 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: solar_nips + dataset: ettm1 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 64 test_batch_size: 64 num_workers: 8 diff --git a/config/baselines/ettm2/autoformer.yaml b/config/baselines/ettm2/autoformer.yaml new file mode 100644 index 0000000..8d86cd5 --- /dev/null +++ b/config/baselines/ettm2/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm2/dlinear.yaml b/config/baselines/ettm2/dlinear.yaml similarity index 100% rename from config/ltsf/ettm2/dlinear.yaml rename to config/baselines/ettm2/dlinear.yaml diff --git a/config/baselines/ettm2/elastst.yaml b/config/baselines/ettm2/elastst.yaml new file mode 100644 index 0000000..2eea5bf --- /dev/null +++ b/config/baselines/ettm2/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 10 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm2/patchtst.yaml b/config/baselines/ettm2/patchtst.yaml similarity index 100% rename from config/ltsf/ettm2/patchtst.yaml rename to config/baselines/ettm2/patchtst.yaml diff --git a/config/baselines/ettm2/transformer_enc.yaml b/config/baselines/ettm2/transformer_enc.yaml new file mode 100644 index 0000000..147323e --- /dev/null +++ b/config/baselines/ettm2/transformer_enc.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerEnc + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/electricity/timesnet.yaml b/config/baselines/ettm2/tsmixer.yaml similarity index 65% rename from config/stsf/electricity/timesnet.yaml rename to config/baselines/ettm2/tsmixer.yaml index e5e0a62..3c19503 100644 --- a/config/stsf/electricity/timesnet.yaml +++ b/config/baselines/ettm2/tsmixer.yaml @@ -4,33 +4,34 @@ trainer: accelerator: gpu devices: 1 strategy: auto - max_epochs: 50 + max_epochs: 40 use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet + class_path: probts.model.forecaster.point_forecaster.TSMixer init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 64 - dropout: 0.1 - f_hidden_size: 64 + num_blocks: 8 + dropout_rate: 0.9 + ff_dim: 256 use_lags: false use_feat_idx_emb: false use_time_feat: false - learning_rate: 0.001 + feat_idx_emb_dim: 1 + learning_rate: 0.0001 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: electricity_nips + dataset: ettm2 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 64 test_batch_size: 64 num_workers: 8 diff --git a/config/baselines/exchange_ltsf/autoformer.yaml b/config/baselines/exchange_ltsf/autoformer.yaml new file mode 100644 index 0000000..33dc944 --- /dev/null +++ b/config/baselines/exchange_ltsf/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/exchange_ltsf/dlinear.yaml b/config/baselines/exchange_ltsf/dlinear.yaml similarity index 100% rename from config/ltsf/exchange_ltsf/dlinear.yaml rename to config/baselines/exchange_ltsf/dlinear.yaml diff --git a/config/baselines/exchange_ltsf/elastst.yaml b/config/baselines/exchange_ltsf/elastst.yaml new file mode 100644 index 0000000..6a235b5 --- /dev/null +++ b/config/baselines/exchange_ltsf/elastst.yaml @@ -0,0 +1,54 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 10 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.1 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + use_norm: true + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/exchange_ltsf/patchtst.yaml b/config/baselines/exchange_ltsf/patchtst.yaml similarity index 100% rename from config/ltsf/exchange_ltsf/patchtst.yaml rename to config/baselines/exchange_ltsf/patchtst.yaml diff --git a/config/baselines/exchange_ltsf/transformer_enc.yaml b/config/baselines/exchange_ltsf/transformer_enc.yaml new file mode 100644 index 0000000..147323e --- /dev/null +++ b/config/baselines/exchange_ltsf/transformer_enc.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerEnc + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/default/timesnet.yaml b/config/baselines/exchange_ltsf/tsmixer.yaml similarity index 65% rename from config/default/timesnet.yaml rename to config/baselines/exchange_ltsf/tsmixer.yaml index 745a424..e911f49 100644 --- a/config/default/timesnet.yaml +++ b/config/baselines/exchange_ltsf/tsmixer.yaml @@ -4,33 +4,34 @@ trainer: accelerator: gpu devices: 1 strategy: auto - max_epochs: 50 + max_epochs: 40 use_distributed_sampler: false limit_train_batches: 100 log_every_n_steps: 1 default_root_dir: ./results + accumulate_grad_batches: 1 model: forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet + class_path: probts.model.forecaster.point_forecaster.TSMixer init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 32 - dropout: 0.1 - f_hidden_size: 40 + num_blocks: 4 + dropout_rate: 0.9 + ff_dim: 32 use_lags: false use_feat_idx_emb: false use_time_feat: false - learning_rate: 0.001 + feat_idx_emb_dim: 1 + learning_rate: 0.0001 quantiles_num: 20 data: data_manager: class_path: probts.data.data_manager.DataManager init_args: - dataset: solar_nips + dataset: ettm2 split_val: true scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 batch_size: 64 test_batch_size: 64 num_workers: 8 diff --git a/config/baselines/traffic_ltsf/autoformer.yaml b/config/baselines/traffic_ltsf/autoformer.yaml new file mode 100644 index 0000000..6d0297e --- /dev/null +++ b/config/baselines/traffic_ltsf/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/traffic_ltsf/dlinear.yaml b/config/baselines/traffic_ltsf/dlinear.yaml similarity index 100% rename from config/ltsf/traffic_ltsf/dlinear.yaml rename to config/baselines/traffic_ltsf/dlinear.yaml diff --git a/config/baselines/traffic_ltsf/elastst.yaml b/config/baselines/traffic_ltsf/elastst.yaml new file mode 100644 index 0000000..70e8aa2 --- /dev/null +++ b/config/baselines/traffic_ltsf/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 128 + d_inner: 128 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 4 + test_batch_size: 4 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/traffic_ltsf/patchtst.yaml b/config/baselines/traffic_ltsf/patchtst.yaml similarity index 97% rename from config/ltsf/traffic_ltsf/patchtst.yaml rename to config/baselines/traffic_ltsf/patchtst.yaml index 6ddf41d..73479f2 100644 --- a/config/ltsf/traffic_ltsf/patchtst.yaml +++ b/config/baselines/traffic_ltsf/patchtst.yaml @@ -22,7 +22,7 @@ model: n_heads: 16 fc_dropout: 0.2 head_dropout: 0 - individual: false + individual: true learning_rate: 0.0001 quantiles_num: 20 data: diff --git a/config/baselines/traffic_ltsf/transformer_enc.yaml b/config/baselines/traffic_ltsf/transformer_enc.yaml new file mode 100644 index 0000000..0589df8 --- /dev/null +++ b/config/baselines/traffic_ltsf/transformer_enc.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerEnc + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/baselines/traffic_ltsf/tsmixer.yaml b/config/baselines/traffic_ltsf/tsmixer.yaml new file mode 100644 index 0000000..31659e1 --- /dev/null +++ b/config/baselines/traffic_ltsf/tsmixer.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TSMixer + init_args: + num_blocks: 8 + dropout_rate: 0.7 + ff_dim: 256 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/baselines/weather_ltsf/autoformer.yaml b/config/baselines/weather_ltsf/autoformer.yaml new file mode 100644 index 0000000..46fc633 --- /dev/null +++ b/config/baselines/weather_ltsf/autoformer.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/weather_ltsf/dlinear.yaml b/config/baselines/weather_ltsf/dlinear.yaml similarity index 100% rename from config/ltsf/weather_ltsf/dlinear.yaml rename to config/baselines/weather_ltsf/dlinear.yaml diff --git a/config/baselines/weather_ltsf/elastst.yaml b/config/baselines/weather_ltsf/elastst.yaml new file mode 100644 index 0000000..d932781 --- /dev/null +++ b/config/baselines/weather_ltsf/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 100 + use_distributed_sampler: false + limit_train_batches: 50 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 32 + test_batch_size: 16 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/weather_ltsf/patchtst.yaml b/config/baselines/weather_ltsf/patchtst.yaml similarity index 100% rename from config/ltsf/weather_ltsf/patchtst.yaml rename to config/baselines/weather_ltsf/patchtst.yaml diff --git a/config/baselines/weather_ltsf/transformer_enc.yaml b/config/baselines/weather_ltsf/transformer_enc.yaml new file mode 100644 index 0000000..97c356b --- /dev/null +++ b/config/baselines/weather_ltsf/transformer_enc.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerEnc + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/baselines/weather_ltsf/tsmixer.yaml b/config/baselines/weather_ltsf/tsmixer.yaml new file mode 100644 index 0000000..b20bb9e --- /dev/null +++ b/config/baselines/weather_ltsf/tsmixer.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TSMixer + init_args: + num_blocks: 8 + dropout_rate: 0.7 + ff_dim: 64 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/csdi.yaml b/config/default/csdi.yaml deleted file mode 100644 index 9c27587..0000000 --- a/config/default/csdi.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 4 - test_batch_size: 4 - num_workers: 8 diff --git a/config/default/dlinear.yaml b/config/default/dlinear.yaml deleted file mode 100644 index 3bd35fe..0000000 --- a/config/default/dlinear.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - learning_rate: 0.01 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/default/gru.yaml b/config/default/gru.yaml deleted file mode 100644 index d1cf880..0000000 --- a/config/default/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 40 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/default/gru_maf.yaml b/config/default/gru_maf.yaml deleted file mode 100644 index 53fee8f..0000000 --- a/config/default/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 40 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - scaler: identity # identity, standard, temporal - split_val: true - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/gru_nvp.yaml b/config/default/gru_nvp.yaml deleted file mode 100644 index 75860b5..0000000 --- a/config/default/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 7 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/linear.yaml b/config/default/linear.yaml deleted file mode 100644 index af0c832..0000000 --- a/config/default/linear.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 30 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.LinearForecaster - init_args: - individual: false - use_lags: true - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/default/mean.yaml b/config/default/mean.yaml deleted file mode 100644 index e114886..0000000 --- a/config/default/mean.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.MeanForecaster - init_args: - mode: global - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/nhits.yaml b/config/default/nhits.yaml deleted file mode 100644 index e6a16ba..0000000 --- a/config/default/nhits.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.NHiTS - init_args: - n_blocks: [1,1,1] - hidden_size: 512 - pooling_mode: 'max' - interpolation_mode: 'linear' - activation: 'ReLU' - initialization: 'lecun_normal' - batch_normalization: false - shared_weights: false - naive_level: - dropout: 0 - n_layers: 2 - use_lags: false - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/default/patchtst.yaml b/config/default/patchtst.yaml deleted file mode 100644 index d8e5551..0000000 --- a/config/default/patchtst.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 3 - patch_len: 6 - dropout: 0.1 - f_hidden_size: 32 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: false - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/timegrad.yaml b/config/default/timegrad.yaml deleted file mode 100644 index 65c1085..0000000 --- a/config/default/timegrad.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - scaler: identity # identity, standard, temporal - split_val: true - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/trans_maf.yaml b/config/default/trans_maf.yaml deleted file mode 100644 index 1641632..0000000 --- a/config/default/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 32 - enc_num_heads: 8 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - scaler: identity # identity, standard, temporal - split_val: true - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/transformer.yaml b/config/default/transformer.yaml deleted file mode 100644 index 00d1fef..0000000 --- a/config/default/transformer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster - init_args: - f_hidden_size: 16 - num_heads: 4 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/default/tsdiff.yaml b/config/default/tsdiff.yaml deleted file mode 100644 index 8e8c77d..0000000 --- a/config/default/tsdiff.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - check_val_every_n_epoch: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 - gradient_clip_val: 0.5 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TSDiffCond - init_args: - timesteps: 100 - hidden_dim: 64 - step_emb: 128 - num_residual_blocks: 3 - dropout: 0.0 - mode: diag # diag, nplr - measure: diag # 'diag', 'diag-lin', 'diag-inv', or 'diag-legs' for diag - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: false - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: temporal # identity, standard, temporal - context_length: 336 - batch_size: 32 - test_batch_size: 32 - num_workers: 8 diff --git a/config/elastst.yaml b/config/elastst.yaml new file mode 100644 index 0000000..653ec0c --- /dev/null +++ b/config/elastst.yaml @@ -0,0 +1,53 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ElasTST + init_args: + l_patch_size: '8_16_32' + dropout: 0.0 + f_hidden_size: 256 + d_inner: 256 + t_layers: 2 + v_layers: 0 + n_heads: 8 + d_v: 64 + d_k: 64 + structured_mask: true + rotate: true + rope_theta_init: 'exp' + learnable_rope: true + min_period: 1 + max_period: 1000 + addv: false + bin_att: false + learn_tem_emb: false + learning_rate: 0.001 + quantiles_num: 20 + sampling_weight_scheme: random +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 24-96-192-336-720-1024 + train_ctx_len_list: 96 + train_pred_len_list: 720 + val_pred_len_list: 720 + continous_sample: false + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/electricity_ltsf/csdi.yaml b/config/ltsf/electricity_ltsf/csdi.yaml deleted file mode 100644 index faa5660..0000000 --- a/config/ltsf/electricity_ltsf/csdi.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 3 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 64 - emb_feature_dim: 8 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 64 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 16 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_ltsf - scaler: standard # identity, standard, temporal - split_val: true - batch_size: 4 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/gru_nvp.yaml b/config/ltsf/electricity_ltsf/gru_nvp.yaml deleted file mode 100644 index cd53158..0000000 --- a/config/ltsf/electricity_ltsf/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 128 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 64 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/timegrad.yaml b/config/ltsf/electricity_ltsf/timegrad.yaml deleted file mode 100644 index e3d1610..0000000 --- a/config/ltsf/electricity_ltsf/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 128 - enc_num_layers: 3 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/etth1/csdi.yaml b/config/ltsf/etth1/csdi.yaml deleted file mode 100644 index b81d015..0000000 --- a/config/ltsf/etth1/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth1 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/etth1/gru_nvp.yaml b/config/ltsf/etth1/gru_nvp.yaml deleted file mode 100644 index e133f74..0000000 --- a/config/ltsf/etth1/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 64 - n_hidden: 3 - batch_norm: false - conditional_length: 100 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth1 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/ltsf/etth1/timegrad.yaml b/config/ltsf/etth1/timegrad.yaml deleted file mode 100644 index 0fad3cb..0000000 --- a/config/ltsf/etth1/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 128 - enc_num_layers: 3 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth1 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/etth2/csdi.yaml b/config/ltsf/etth2/csdi.yaml deleted file mode 100644 index 122f487..0000000 --- a/config/ltsf/etth2/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth2 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/etth2/gru_nvp.yaml b/config/ltsf/etth2/gru_nvp.yaml deleted file mode 100644 index e2db668..0000000 --- a/config/ltsf/etth2/gru_nvp.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 128 - n_hidden: 3 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth2 - path: /home/covpreduser/Blob/v-jiawezhang/data/all_datasets/ - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/etth2/timegrad.yaml b/config/ltsf/etth2/timegrad.yaml deleted file mode 100644 index dad8862..0000000 --- a/config/ltsf/etth2/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth2 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/ettm1/csdi.yaml b/config/ltsf/ettm1/csdi.yaml deleted file mode 100644 index ccf7b4c..0000000 --- a/config/ltsf/ettm1/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm1 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/ettm1/gru_nvp.yaml b/config/ltsf/ettm1/gru_nvp.yaml deleted file mode 100644 index 351e97d..0000000 --- a/config/ltsf/ettm1/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 64 - n_hidden: 3 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm1 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/ettm1/timegrad.yaml b/config/ltsf/ettm1/timegrad.yaml deleted file mode 100644 index 093c27a..0000000 --- a/config/ltsf/ettm1/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 128 - enc_num_layers: 3 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm1 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/ettm2/csdi.yaml b/config/ltsf/ettm2/csdi.yaml deleted file mode 100644 index 87a147e..0000000 --- a/config/ltsf/ettm2/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm2 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/ettm2/gru_nvp.yaml b/config/ltsf/ettm2/gru_nvp.yaml deleted file mode 100644 index 47ab9b4..0000000 --- a/config/ltsf/ettm2/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 128 - n_hidden: 3 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm2 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/ettm2/timegrad.yaml b/config/ltsf/ettm2/timegrad.yaml deleted file mode 100644 index cafdd05..0000000 --- a/config/ltsf/ettm2/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 64 - enc_num_layers: 2 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm2 - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/csdi.yaml b/config/ltsf/exchange_ltsf/csdi.yaml deleted file mode 100644 index f15e122..0000000 --- a/config/ltsf/exchange_ltsf/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_ltsf - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/gru_nvp.yaml b/config/ltsf/exchange_ltsf/gru_nvp.yaml deleted file mode 100644 index 595b9e5..0000000 --- a/config/ltsf/exchange_ltsf/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 128 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 128 - n_hidden: 3 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/timegrad.yaml b/config/ltsf/exchange_ltsf/timegrad.yaml deleted file mode 100644 index ce7b6a5..0000000 --- a/config/ltsf/exchange_ltsf/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/illness_ltsf/csdi.yaml b/config/ltsf/illness_ltsf/csdi.yaml deleted file mode 100644 index 97a72eb..0000000 --- a/config/ltsf/illness_ltsf/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: illness_ltsf - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/illness_ltsf/gru_nvp.yaml b/config/ltsf/illness_ltsf/gru_nvp.yaml deleted file mode 100644 index 5926412..0000000 --- a/config/ltsf/illness_ltsf/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 128 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: illness_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 36 - prediction_length: 36 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/illness_ltsf/patchtst.yaml b/config/ltsf/illness_ltsf/patchtst.yaml deleted file mode 100644 index 729ec2f..0000000 --- a/config/ltsf/illness_ltsf/patchtst.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 2 - patch_len: 24 - dropout: 0.3 - f_hidden_size: 16 - n_layers: 3 - n_heads: 4 - fc_dropout: 0.3 - head_dropout: 0 - individual: true - learning_rate: 0.0025 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: illness_ltsf - path: /home/covpreduser/Blob/v-jiawezhang/data/all_datasets/ - split_val: true - scaler: standard # identity, standard, temporal - context_length: 36 - prediction_length: 36 - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/illness_ltsf/timegrad.yaml b/config/ltsf/illness_ltsf/timegrad.yaml deleted file mode 100644 index 03fe6aa..0000000 --- a/config/ltsf/illness_ltsf/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 64 - enc_num_layers: 2 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: illness_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 36 - prediction_length: 36 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/csdi.yaml b/config/ltsf/traffic_ltsf/csdi.yaml deleted file mode 100644 index 9fe882f..0000000 --- a/config/ltsf/traffic_ltsf/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 3 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 64 - emb_feature_dim: 8 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 64 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 16 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_ltsf - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 4 - test_batch_size: 4 - num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/gru_nvp.yaml b/config/ltsf/traffic_ltsf/gru_nvp.yaml deleted file mode 100644 index d4cd530..0000000 --- a/config/ltsf/traffic_ltsf/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 128 - enc_num_layers: 3 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 128 - n_hidden: 3 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/timegrad.yaml b/config/ltsf/traffic_ltsf/timegrad.yaml deleted file mode 100644 index 682500a..0000000 --- a/config/ltsf/traffic_ltsf/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 128 - enc_num_layers: 3 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/weather_ltsf/csdi.yaml b/config/ltsf/weather_ltsf/csdi.yaml deleted file mode 100644 index 0c66d4d..0000000 --- a/config/ltsf/weather_ltsf/csdi.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 1 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 128 - emb_feature_dim: 16 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 128 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: weather_ltsf - split_val: true - scaler: standard # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/ltsf/weather_ltsf/gru_nvp.yaml b/config/ltsf/weather_ltsf/gru_nvp.yaml deleted file mode 100644 index 0d5a178..0000000 --- a/config/ltsf/weather_ltsf/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 128 - n_hidden: 3 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: weather_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/ltsf/weather_ltsf/timegrad.yaml b/config/ltsf/weather_ltsf/timegrad.yaml deleted file mode 100644 index ffcd86a..0000000 --- a/config/ltsf/weather_ltsf/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 200 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: weather_ltsf - split_val: true - scaler: identity # identity, standard, temporal - context_length: 96 - prediction_length: 96 - batch_size: 16 - test_batch_size: 16 - num_workers: 8 diff --git a/config/m4/m4_daily/csdi.yaml b/config/m4/m4_daily/csdi.yaml deleted file mode 100644 index 5e5e202..0000000 --- a/config/m4/m4_daily/csdi.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 32 - emb_feature_dim: 4 - channels: 16 - n_layers: 4 - num_heads: 4 - num_steps: 50 - diffusion_embedding_dim: 32 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_daily - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_daily/dlinear.yaml b/config/m4/m4_daily/dlinear.yaml deleted file mode 100644 index 161b6dd..0000000 --- a/config/m4/m4_daily/dlinear.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_daily - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_daily/gru_nvp.yaml b/config/m4/m4_daily/gru_nvp.yaml deleted file mode 100644 index 09d31a5..0000000 --- a/config/m4/m4_daily/gru_nvp.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 100 - dequantize: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_daily - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_daily/patchtst.yaml b/config/m4/m4_daily/patchtst.yaml deleted file mode 100644 index e97e8ef..0000000 --- a/config/m4/m4_daily/patchtst.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 2 - patch_len: 6 - dropout: 0.3 - f_hidden_size: 32 - d_ff: 128 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_daily - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 128 - num_workers: 8 \ No newline at end of file diff --git a/config/m4/m4_daily/timegrad.yaml b/config/m4/m4_daily/timegrad.yaml deleted file mode 100644 index 376f3c4..0000000 --- a/config/m4/m4_daily/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 50 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_daily - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_weekly/csdi.yaml b/config/m4/m4_weekly/csdi.yaml deleted file mode 100644 index ad4d95c..0000000 --- a/config/m4/m4_weekly/csdi.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 32 - emb_feature_dim: 4 - channels: 16 - n_layers: 4 - num_heads: 4 - num_steps: 50 - diffusion_embedding_dim: 32 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_weekly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_weekly/dlinear.yaml b/config/m4/m4_weekly/dlinear.yaml deleted file mode 100644 index 3b3b918..0000000 --- a/config/m4/m4_weekly/dlinear.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_weekly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_weekly/gru_nvp.yaml b/config/m4/m4_weekly/gru_nvp.yaml deleted file mode 100644 index 2f372c0..0000000 --- a/config/m4/m4_weekly/gru_nvp.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 100 - dequantize: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_weekly - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m4_weekly/patchtst.yaml b/config/m4/m4_weekly/patchtst.yaml deleted file mode 100644 index 2c63319..0000000 --- a/config/m4/m4_weekly/patchtst.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 3 - patch_len: 6 - dropout: 0.3 - f_hidden_size: 32 - d_ff: 128 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_weekly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 128 - num_workers: 8 \ No newline at end of file diff --git a/config/m4/m4_weekly/timegrad.yaml b/config/m4/m4_weekly/timegrad.yaml deleted file mode 100644 index 43e873c..0000000 --- a/config/m4/m4_weekly/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 50 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m4_weekly - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m5/csdi.yaml b/config/m4/m5/csdi.yaml deleted file mode 100644 index aa8dee7..0000000 --- a/config/m4/m5/csdi.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 32 - emb_feature_dim: 4 - channels: 16 - n_layers: 4 - num_heads: 4 - num_steps: 50 - diffusion_embedding_dim: 32 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m5 - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m5/dlinear.yaml b/config/m4/m5/dlinear.yaml deleted file mode 100644 index 848f9a0..0000000 --- a/config/m4/m5/dlinear.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m5 - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 256 - num_workers: 8 diff --git a/config/m4/m5/gru_nvp.yaml b/config/m4/m5/gru_nvp.yaml deleted file mode 100644 index 71a8a9a..0000000 --- a/config/m4/m5/gru_nvp.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 100 - dequantize: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m5 - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/m5/patchtst.yaml b/config/m4/m5/patchtst.yaml deleted file mode 100644 index 6fbe3b1..0000000 --- a/config/m4/m5/patchtst.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 2 - patch_len: 4 - dropout: 0.3 - f_hidden_size: 64 - d_ff: 128 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m5 - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 128 - num_workers: 8 \ No newline at end of file diff --git a/config/m4/m5/timegrad.yaml b/config/m4/m5/timegrad.yaml deleted file mode 100644 index 2437872..0000000 --- a/config/m4/m5/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 30 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 50 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: m5 - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 512 - num_workers: 8 diff --git a/config/m4/tourism_monthly/csdi.yaml b/config/m4/tourism_monthly/csdi.yaml deleted file mode 100644 index ed49c8e..0000000 --- a/config/m4/tourism_monthly/csdi.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 32 - emb_feature_dim: 4 - channels: 16 - n_layers: 4 - num_heads: 4 - num_steps: 50 - diffusion_embedding_dim: 32 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 64 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: tourism_monthly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/tourism_monthly/dlinear.yaml b/config/m4/tourism_monthly/dlinear.yaml deleted file mode 100644 index 4181943..0000000 --- a/config/m4/tourism_monthly/dlinear.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: tourism_monthly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/tourism_monthly/gru_nvp.yaml b/config/m4/tourism_monthly/gru_nvp.yaml deleted file mode 100644 index fae0350..0000000 --- a/config/m4/tourism_monthly/gru_nvp.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 2 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 100 - dequantize: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: tourism_monthly - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/m4/tourism_monthly/patchtst.yaml b/config/m4/tourism_monthly/patchtst.yaml deleted file mode 100644 index de72060..0000000 --- a/config/m4/tourism_monthly/patchtst.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 2 - patch_len: 6 - dropout: 0.3 - f_hidden_size: 64 - d_ff: 128 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: tourism_monthly - context_length_factor: 3 - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 128 - num_workers: 8 \ No newline at end of file diff --git a/config/m4/tourism_monthly/timegrad.yaml b/config/m4/tourism_monthly/timegrad.yaml deleted file mode 100644 index 848e726..0000000 --- a/config/m4/tourism_monthly/timegrad.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 800 - log_every_n_steps: 2 - check_val_every_n_epoch: 2 - default_root_dir: ./results - accumulate_grad_batches: 8 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 50 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 64 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: tourism_monthly - context_length_factor: 3 - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 diff --git a/config/pipeline_config.yaml b/config/pipeline_config.yaml deleted file mode 100644 index 2dcdd3e..0000000 --- a/config/pipeline_config.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# lightning.pytorch==2.3.0dev -seed_everything: true -trainer: - accelerator: auto - strategy: auto - devices: auto - num_nodes: 1 - precision: null - logger: null - callbacks: null - fast_dev_run: false - max_epochs: null - min_epochs: null - max_steps: -1 - min_steps: null - max_time: null - limit_train_batches: null - limit_val_batches: null - limit_test_batches: null - limit_predict_batches: null - overfit_batches: 0.0 - val_check_interval: null - check_val_every_n_epoch: 1 - num_sanity_val_steps: null - log_every_n_steps: null - enable_checkpointing: null - enable_progress_bar: null - enable_model_summary: null - accumulate_grad_batches: 1 - gradient_clip_val: null - gradient_clip_algorithm: null - deterministic: null - benchmark: null - inference_mode: true - use_distributed_sampler: true - profiler: null - detect_anomaly: false - barebones: false - plugins: null - sync_batchnorm: false - reload_dataloaders_every_n_epochs: 0 - default_root_dir: null -model: - forecaster: null - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 10 - load_from_ckpt: null -data: - data_manager: null - batch_size: 64 - test_batch_size: 8 - num_workers: 8 diff --git a/config/stsf/electricity/gru.yaml b/config/stsf/electricity/gru.yaml deleted file mode 100644 index 24c9072..0000000 --- a/config/stsf/electricity/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 40 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/electricity/gru_maf.yaml b/config/stsf/electricity/gru_maf.yaml deleted file mode 100644 index c39a2bf..0000000 --- a/config/stsf/electricity/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 40 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/electricity/gru_nvp.yaml b/config/stsf/electricity/gru_nvp.yaml deleted file mode 100644 index ad2c480..0000000 --- a/config/stsf/electricity/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/electricity/patchtst.yaml b/config/stsf/electricity/patchtst.yaml deleted file mode 100644 index 5a1d4d8..0000000 --- a/config/stsf/electricity/patchtst.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 2 - patch_len: 4 - dropout: 0.1 - f_hidden_size: 64 - n_layers: 4 - n_heads: 8 - fc_dropout: 0.1 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/electricity/timegrad.yaml b/config/stsf/electricity/timegrad.yaml deleted file mode 100644 index aaecf63..0000000 --- a/config/stsf/electricity/timegrad.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/electricity/trans_maf.yaml b/config/stsf/electricity/trans_maf.yaml deleted file mode 100644 index 1c61bb0..0000000 --- a/config/stsf/electricity/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 32 - enc_num_heads: 8 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/electricity/transformer.yaml b/config/stsf/electricity/transformer.yaml deleted file mode 100644 index 667ad85..0000000 --- a/config/stsf/electricity/transformer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster - init_args: - f_hidden_size: 32 - num_heads: 8 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/exchange/dlinear.yaml b/config/stsf/exchange/dlinear.yaml deleted file mode 100644 index 56eccc3..0000000 --- a/config/stsf/exchange/dlinear.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.01 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/gru.yaml b/config/stsf/exchange/gru.yaml deleted file mode 100644 index 411cb52..0000000 --- a/config/stsf/exchange/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 40 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/exchange/gru_maf.yaml b/config/stsf/exchange/gru_maf.yaml deleted file mode 100644 index c30ef96..0000000 --- a/config/stsf/exchange/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 40 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/exchange/gru_nvp.yaml b/config/stsf/exchange/gru_nvp.yaml deleted file mode 100644 index 4b05825..0000000 --- a/config/stsf/exchange/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/exchange/patchtst.yaml b/config/stsf/exchange/patchtst.yaml deleted file mode 100644 index f773b9d..0000000 --- a/config/stsf/exchange/patchtst.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 3 - patch_len: 6 - dropout: 0.1 - f_hidden_size: 32 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/timegrad.yaml b/config/stsf/exchange/timegrad.yaml deleted file mode 100644 index 554e59b..0000000 --- a/config/stsf/exchange/timegrad.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/exchange/trans_maf.yaml b/config/stsf/exchange/trans_maf.yaml deleted file mode 100644 index e39513e..0000000 --- a/config/stsf/exchange/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 16 - enc_num_heads: 8 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/transformer.yaml b/config/stsf/exchange/transformer.yaml deleted file mode 100644 index 1d4ab67..0000000 --- a/config/stsf/exchange/transformer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster - init_args: - f_hidden_size: 32 - num_heads: 8 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/dlinear.yaml b/config/stsf/solar/dlinear.yaml deleted file mode 100644 index 3bd35fe..0000000 --- a/config/stsf/solar/dlinear.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - learning_rate: 0.01 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/gru.yaml b/config/stsf/solar/gru.yaml deleted file mode 100644 index d1cf880..0000000 --- a/config/stsf/solar/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 40 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/solar/gru_maf.yaml b/config/stsf/solar/gru_maf.yaml deleted file mode 100644 index e974359..0000000 --- a/config/stsf/solar/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 40 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/solar/gru_nvp.yaml b/config/stsf/solar/gru_nvp.yaml deleted file mode 100644 index 7d3f8c3..0000000 --- a/config/stsf/solar/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/solar/patchtst.yaml b/config/stsf/solar/patchtst.yaml deleted file mode 100644 index e190df7..0000000 --- a/config/stsf/solar/patchtst.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 3 - patch_len: 6 - dropout: 0.1 - f_hidden_size: 32 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: true - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/timegrad.yaml b/config/stsf/solar/timegrad.yaml deleted file mode 100644 index 9681ae0..0000000 --- a/config/stsf/solar/timegrad.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/solar/trans_maf.yaml b/config/stsf/solar/trans_maf.yaml deleted file mode 100644 index 191c620..0000000 --- a/config/stsf/solar/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 32 - enc_num_heads: 8 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: false - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/transformer.yaml b/config/stsf/solar/transformer.yaml deleted file mode 100644 index 00d1fef..0000000 --- a/config/stsf/solar/transformer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster - init_args: - f_hidden_size: 16 - num_heads: 4 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/csdi.yaml b/config/stsf/traffic/csdi.yaml deleted file mode 100644 index 8451175..0000000 --- a/config/stsf/traffic/csdi.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - check_val_every_n_epoch: 3 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 64 - emb_feature_dim: 8 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 64 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 16 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/stsf/traffic/dlinear.yaml b/config/stsf/traffic/dlinear.yaml deleted file mode 100644 index 6bd4524..0000000 --- a/config/stsf/traffic/dlinear.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/gru.yaml b/config/stsf/traffic/gru.yaml deleted file mode 100644 index 8613675..0000000 --- a/config/stsf/traffic/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 128 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 diff --git a/config/stsf/traffic/gru_maf.yaml b/config/stsf/traffic/gru_maf.yaml deleted file mode 100644 index ea9c941..0000000 --- a/config/stsf/traffic/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 128 - enc_dropout: 0.3 - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/gru_nvp.yaml b/config/stsf/traffic/gru_nvp.yaml deleted file mode 100644 index eef657c..0000000 --- a/config/stsf/traffic/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 128 - enc_num_layers: 2 - enc_dropout: 0.3 - n_blocks: 4 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/patchtst.yaml b/config/stsf/traffic/patchtst.yaml deleted file mode 100644 index 7790448..0000000 --- a/config/stsf/traffic/patchtst.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 1 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 3 - patch_len: 6 - dropout: 0.1 - f_hidden_size: 32 - n_layers: 3 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: false - num_samples: 100 - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/timegrad.yaml b/config/stsf/traffic/timegrad.yaml deleted file mode 100644 index 36caadf..0000000 --- a/config/stsf/traffic/timegrad.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 diff --git a/config/stsf/traffic/timesnet.yaml b/config/stsf/traffic/timesnet.yaml deleted file mode 100644 index 96b0cca..0000000 --- a/config/stsf/traffic/timesnet.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet - init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 16 - dropout: 0.1 - f_hidden_size: 16 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/traffic/trans_maf.yaml b/config/stsf/traffic/trans_maf.yaml deleted file mode 100644 index 0ffb6fd..0000000 --- a/config/stsf/traffic/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 128 - enc_num_heads: 4 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: false - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: traffic_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/csdi.yaml b/config/stsf/wiki/csdi.yaml deleted file mode 100644 index 861f4b2..0000000 --- a/config/stsf/wiki/csdi.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - check_val_every_n_epoch: 3 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.CSDI - init_args: - emb_time_dim: 64 - emb_feature_dim: 8 - channels: 64 - n_layers: 4 - num_heads: 8 - num_steps: 50 - diffusion_embedding_dim: 64 - beta_start: 0.001 - beta_end: 0.5 - sample_size: 16 - linear_trans: false - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - feat_idx_emb_dim: 1 - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 8 - test_batch_size: 8 - num_workers: 8 diff --git a/config/stsf/wiki/dlinear.yaml b/config/stsf/wiki/dlinear.yaml deleted file mode 100644 index 9de36cc..0000000 --- a/config/stsf/wiki/dlinear.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.DLinear - init_args: - individual: false - kernel_size: 3 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 32 - test_batch_size: 32 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/gru.yaml b/config/stsf/wiki/gru.yaml deleted file mode 100644 index 9050ba2..0000000 --- a/config/stsf/wiki/gru.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.GRUForecaster - init_args: - f_hidden_size: 40 - num_layers: 2 - dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/wiki/gru_maf.yaml b/config/stsf/wiki/gru_maf.yaml deleted file mode 100644 index 14a509e..0000000 --- a/config/stsf/wiki/gru_maf.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_MAF - init_args: - enc_num_layers: 2 - enc_hidden_size: 40 - enc_dropout: 0.1 - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/wiki/gru_nvp.yaml b/config/stsf/wiki/gru_nvp.yaml deleted file mode 100644 index 968a739..0000000 --- a/config/stsf/wiki/gru_nvp.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.GRU_NVP - init_args: - enc_hidden_size: 40 - enc_num_layers: 2 - enc_dropout: 0.1 - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/wiki/patchtst.yaml b/config/stsf/wiki/patchtst.yaml deleted file mode 100644 index 0eb87a9..0000000 --- a/config/stsf/wiki/patchtst.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 400 - log_every_n_steps: 1 - default_root_dir: ./results - accumulate_grad_batches: 4 -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.PatchTST - init_args: - stride: 4 - patch_len: 8 - dropout: 0.1 - f_hidden_size: 32 - n_layers: 2 - n_heads: 8 - fc_dropout: 0.2 - head_dropout: 0 - individual: false - num_samples: 100 - learning_rate: 0.0001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 16 - test_batch_size: 16 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/timegrad.yaml b/config/stsf/wiki/timegrad.yaml deleted file mode 100644 index ff8a685..0000000 --- a/config/stsf/wiki/timegrad.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.TimeGrad - init_args: - loss_type: l2 - diff_steps: 100 - beta_end: 0.1 - beta_schedule: linear - conditional_length: 100 - enc_hidden_size: 128 - enc_num_layers: 4 - enc_dropout: 0.1 - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/wiki/timesnet.yaml b/config/stsf/wiki/timesnet.yaml deleted file mode 100644 index 78bc757..0000000 --- a/config/stsf/wiki/timesnet.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesNet - init_args: - n_layers: 2 - num_kernels: 6 - top_k: 5 - d_ff: 32 - dropout: 0.1 - f_hidden_size: 32 - use_lags: false - use_feat_idx_emb: false - use_time_feat: false - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 diff --git a/config/stsf/wiki/trans_maf.yaml b/config/stsf/wiki/trans_maf.yaml deleted file mode 100644 index 2f5afd5..0000000 --- a/config/stsf/wiki/trans_maf.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Trans_MAF - init_args: - enc_hidden_size: 128 - enc_num_heads: 4 - enc_num_encoder_layers: 2 - enc_num_decoder_layers: 2 - enc_dim_feedforward_scale: 4 - enc_dropout: 0.1 - enc_activation: gelu - n_blocks: 3 - hidden_size: 100 - n_hidden: 2 - batch_norm: true - conditional_length: 200 - dequantize: true - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - use_scaling: true - num_samples: 100 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/transformer.yaml b/config/stsf/wiki/transformer.yaml deleted file mode 100644 index 29f6958..0000000 --- a/config/stsf/wiki/transformer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 1 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 50 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TransformerForecaster - init_args: - f_hidden_size: 32 - num_heads: 8 - num_encoder_layers: 3 - num_decoder_layers: 3 - dim_feedforward_scale: 4 - dropout: 0.1 - activation: gelu - use_lags: true - use_feat_idx_emb: true - use_time_feat: true - feat_idx_emb_dim: 1 - learning_rate: 0.001 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: wiki2000_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/chronos.yaml b/config/tsfm/chronos.yaml deleted file mode 100644 index c546ae3..0000000 --- a/config/tsfm/chronos.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Chronos - init_args: - model_size: base - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/forecastpfn.yaml b/config/tsfm/forecastpfn.yaml deleted file mode 100644 index 648733e..0000000 --- a/config/tsfm/forecastpfn.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.ForecastPFN - init_args: - label_len: 48 - ckpt_path: ./checkpoints/ForecastPFN/saved_weights - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - timeenc: 2 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/lag_llama.yaml b/config/tsfm/lag_llama.yaml deleted file mode 100644 index c3d481a..0000000 --- a/config/tsfm/lag_llama.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.LagLlama - init_args: - use_rope_scaling: true - ckpt_path: ./checkpoints/lag-llama/lag-llama.ckpt - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - timeenc: 2 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai.yaml b/config/tsfm/moirai.yaml deleted file mode 100644 index 86f273a..0000000 --- a/config/tsfm/moirai.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/electricity_ltsf.yaml b/config/tsfm/moirai/context_5000/electricity_ltsf.yaml deleted file mode 100644 index 497183b..0000000 --- a/config/tsfm/moirai/context_5000/electricity_ltsf.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: 128 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_ltsf - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 5000 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/electricity_nips.yaml b/config/tsfm/moirai/context_5000/electricity_nips.yaml deleted file mode 100644 index 33cb687..0000000 --- a/config/tsfm/moirai/context_5000/electricity_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: 64 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: true - context_length: 3800 # maximum history length - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/etth1.yaml b/config/tsfm/moirai/context_5000/etth1.yaml deleted file mode 100644 index e2a81a2..0000000 --- a/config/tsfm/moirai/context_5000/etth1.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: 64 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth1 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 5000 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/etth2.yaml b/config/tsfm/moirai/context_5000/etth2.yaml deleted file mode 100644 index d30290d..0000000 --- a/config/tsfm/moirai/context_5000/etth2.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: 64 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth2 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 5000 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/ettm1.yaml b/config/tsfm/moirai/context_5000/ettm1.yaml deleted file mode 100644 index c4e701f..0000000 --- a/config/tsfm/moirai/context_5000/ettm1.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: 64 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm1 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 5000 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/ettm2.yaml b/config/tsfm/moirai/context_5000/ettm2.yaml deleted file mode 100644 index 16ea166..0000000 --- a/config/tsfm/moirai/context_5000/ettm2.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: 128 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm2 - split_val: true - scaler: standard # identity, standard, temporal - context_length: 5000 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/exchange_rate_nips.yaml b/config/tsfm/moirai/context_5000/exchange_rate_nips.yaml deleted file mode 100644 index 4381491..0000000 --- a/config/tsfm/moirai/context_5000/exchange_rate_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: 128 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 5000 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/solar_nips.yaml b/config/tsfm/moirai/context_5000/solar_nips.yaml deleted file mode 100644 index d804b37..0000000 --- a/config/tsfm/moirai/context_5000/solar_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - var_specific_norm: false - context_length: 5000 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_5000/weather_ltsf.yaml b/config/tsfm/moirai/context_5000/weather_ltsf.yaml deleted file mode 100644 index 65ef082..0000000 --- a/config/tsfm/moirai/context_5000/weather_ltsf.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: 128 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: weather_ltsf - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: true - context_length: 5000 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/electricity_ltsf.yaml b/config/tsfm/moirai/context_96/electricity_ltsf.yaml deleted file mode 100644 index c162f4d..0000000 --- a/config/tsfm/moirai/context_96/electricity_ltsf.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_ltsf - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 4 - test_batch_size: 4 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/electricity_nips.yaml b/config/tsfm/moirai/context_96/electricity_nips.yaml deleted file mode 100644 index 061856d..0000000 --- a/config/tsfm/moirai/context_96/electricity_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: 64 - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: electricity_nips - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: true - context_length: 96 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/etth1.yaml b/config/tsfm/moirai/context_96/etth1.yaml deleted file mode 100644 index 315a1b9..0000000 --- a/config/tsfm/moirai/context_96/etth1.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth1 - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/etth2.yaml b/config/tsfm/moirai/context_96/etth2.yaml deleted file mode 100644 index 7e4457a..0000000 --- a/config/tsfm/moirai/context_96/etth2.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: etth2 - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/ettm1.yaml b/config/tsfm/moirai/context_96/ettm1.yaml deleted file mode 100644 index 54198fb..0000000 --- a/config/tsfm/moirai/context_96/ettm1.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm1 - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/ettm2.yaml b/config/tsfm/moirai/context_96/ettm2.yaml deleted file mode 100644 index aa45204..0000000 --- a/config/tsfm/moirai/context_96/ettm2.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: ettm2 - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/exchange_rate_nips.yaml b/config/tsfm/moirai/context_96/exchange_rate_nips.yaml deleted file mode 100644 index d0dce89..0000000 --- a/config/tsfm/moirai/context_96/exchange_rate_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: exchange_rate_nips - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: true - context_length: 96 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/solar_nips.yaml b/config/tsfm/moirai/context_96/solar_nips.yaml deleted file mode 100644 index 16a19b8..0000000 --- a/config/tsfm/moirai/context_96/solar_nips.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: S - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - var_specific_norm: false - context_length: 96 - batch_size: 1 - test_batch_size: 1 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai/context_96/weather_ltsf.yaml b/config/tsfm/moirai/context_96/weather_ltsf.yaml deleted file mode 100644 index 6c495c2..0000000 --- a/config/tsfm/moirai/context_96/weather_ltsf.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 1 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.prob_forecaster.Moirai - init_args: - variate_mode: M - patch_size: auto - model_size: base - scaling: true - num_samples: 100 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: weather_ltsf - split_val: true - scaler: standard # identity, standard, temporal - var_specific_norm: true - context_length: 96 - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/timer.yaml b/config/tsfm/timer.yaml deleted file mode 100644 index 4e1fcb7..0000000 --- a/config/tsfm/timer.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.Timer - init_args: - label_len: 96 - ckpt_path: ./checkpoints/timer/Timer_67M_UTSD_4G.pt - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/timesfm.yaml b/config/tsfm/timesfm.yaml deleted file mode 100644 index e0a4a17..0000000 --- a/config/tsfm/timesfm.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TimesFM - init_args: - input_patch_len: 32 - output_patch_len: 128 - num_layers: 20 - model_dims: 1280 - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: identity # identity, standard, temporal - var_specific_norm: true - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/tinytimemixer.yaml b/config/tsfm/tinytimemixer.yaml deleted file mode 100644 index 6ba051a..0000000 --- a/config/tsfm/tinytimemixer.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.TinyTimeMixer - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/units.yaml b/config/tsfm/units.yaml deleted file mode 100644 index a0583dc..0000000 --- a/config/tsfm/units.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# lightning==2.3.0.dev0 -seed_everything: 0 -trainer: - accelerator: gpu - devices: 1 - strategy: auto - max_epochs: 40 - use_distributed_sampler: false - limit_train_batches: 100 - log_every_n_steps: 1 - default_root_dir: ./results -model: - forecaster: - class_path: probts.model.forecaster.point_forecaster.UniTS - init_args: - ckpt_path: ./checkpoints/units/units_x128_pretrain_checkpoint.pth - quantiles_num: 20 -data: - data_manager: - class_path: probts.data.data_manager.DataManager - init_args: - dataset: solar_nips - split_val: true - scaler: standard # identity, standard, temporal - # var_norm: true - batch_size: 64 - test_batch_size: 64 - num_workers: 8 \ No newline at end of file diff --git a/docs/benchmark/FOUNDATION_MODEL.md b/docs/benchmark/FOUNDATION_MODEL.md deleted file mode 100644 index e489e16..0000000 --- a/docs/benchmark/FOUNDATION_MODEL.md +++ /dev/null @@ -1,32 +0,0 @@ -## Time Series Foundation Models Benchmarking - - - - -We have incorporated eight recently emerged time series foundation models, namely [Lag-Llama](https://github.com/time-series-foundation-models/lag-llama), [Chronos](https://github.com/amazon-science/chronos-forecasting), [TimesFM](https://github.com/google-research/timesfm), [Timer](https://github.com/thuml/Large-Time-Series-Model), [MOIRAI](https://github.com/SalesforceAIResearch/uni2ts), [UniTS](https://github.com/mims-harvard/UniTS), [ForecastPFN](https://github.com/abacusai/ForecastPFN), and [TTM](https://github.com/ibm-granite/granite-tsfm), into our framework. These foundation models are categorized based on their capabilities, such as zero-shot forecasting, adaptability to varying prediction lengths, and support for probabilistic predictions, as well as their architectural designs, including whether they are auto-regressive and the nature of their backbone networks. Additionally, we have detailed their training processes, including the lengths of prediction horizons used during pre-training and the sizes of look-back windows. - -Detailed configuration files can be found in folder [config/tsfm/](../../config/tsfm/). - -### A Comparison of Pre-trained Time-series Foundation Models - -Table 1. Foundation Models for Time Series. **Zero-shot** indicates whether the original work tests zero-shot capabilities. **Any-horizon** indicates if the same pre-trained model can adapt to prediction tasks of varying lengths. **AR** denotes if the model performs auto-regressive forecasting. **Prob.** indicates if the model natively supports probabilistic forecasting. **Arch.** denotes the model's backbone architecture: D-O for decoder-only transformer, E-O for encoder-only transformer, E-D for encoder-decoder transformer, and unique for specially designed backbones. **Multi-variate** indicates if the model explicitly handles multivariate relationships. **Pre-train Horizons** specifies the forecasting task horizons during pre-training. **Look-back Window** specifies the context history length settings used in the original experiments. - -![Foundation model summary](./figs/FM_summary.jpg) - -Table 2. Evaluation Datasets for Time-series Foundation Models. We selected several popular datasets to evaluate time-series foundation models. \checkmark indicates pre-training on the dataset, $\bigcirc$ indicates zero-shot evaluation on the dataset, few indicates few-shot evaluation on the dataset, and ✗ indicates the dataset is not mentioned in the paper or documentation. ‘*’ indicates that the data comes from the same source but may be processed differently. - - -![pre-train datasets summary](./figs/FM_dataset.jpg) - - -### Comparison of Time-series Foundation Models on Diverse Prediction Horizons - -Table 3. NMAE of time-series foundation models on diverse prediction horizons. The input sequence length is set to 96 if not specified. For every model, we exclude the evaluation results on its pre-trained datasets - -![Comparison of Time-series Foundation Models on Diverse Prediction Horizons](./figs/fm_var_hor.jpg) - -### Comparison of Time-series Foundation Models on Short-term Probabilistic Forecasting - -Table 4. Results of probabilistic foundation models on short-term distributional forecasting. For every model, we exclude the evaluation results on its pre-trained datasets. - -![Comparison of Time-series Foundation Models on short-term scenerio](./figs/fm_short_term.jpg) diff --git a/docs/benchmark/README.md b/docs/benchmark/README.md deleted file mode 100644 index 76ca2a8..0000000 --- a/docs/benchmark/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Benchmarking :balance_scale: - -We conducted a comprehensive benchmarking and analysis of a diverse range of state-of-the-art models from different strands of research. We mainly assessed these models using NAME and CRPS metrics across multiple forecasting horizons, repeating each experiment five times with different seeds to ensure result reliability. - -Results of **time series foundation models** see [HERE](./FOUNDATION_MODEL.md). - -## Long-term Forecasting Benchmarking - -Detailed configuration files can be found in folder [config/ltsf/](../../config/ltsf/). - -Table 1. Results ($\textrm{mean}_{\textrm{std}}$) on long-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds. The input sequence length is set to 36 for the ILI-L dataset and 96 for the others. Due to the excessive time and memory consumption of CSDI in producing long-term forecasts, its results are unavailable in some datasets. - -![long-term forecasting experimental results](./figs/long_bench.jpg) - - - -## Short-term Forecasting Benchmarking - -Detailed configuration files can be found in folder [config/stsf/](../../config/stsf/). - -Table 2.Results ($\textrm{mean}_{\textrm{std}}$) on short-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds. - -![short-term forecasting experimental results](./figs/short_bench.jpg) diff --git a/docs/benchmark/figs/FM_dataset.jpg b/docs/benchmark/figs/FM_dataset.jpg deleted file mode 100644 index 6e2d8c751237195ea92bdd996b7cbdfbaadfacc5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 268047 zcmeEvcT`i`*6*fCH*};1DWVjSCa6e2q=^_oArvJ_ktRkFBuY$>-a`>Mf&zkIp-C4+ zB3(cQL0Ui{38GRHz!*&kZ+p(Y-+jkB#=Gx(3E2L}Myfd7Ck5@2SD_P+oC_V$1V004XdN1Q*v4feo809mjB0B0fRKlcc< z6>|Nv&pGn*!!0tWnXQEd@}wi&%J#VBpQq;BXMXgEC8r1g1YHUVcC#+}_5;I0E`$3y?t*;@&%i(XPk;7<5EF zfX8h7Jl^-uJ}2MLzUN%soMej!SF`}XZqGWSM#`Y4^cel^4= z=&F+KB`;L)WmNF_5Fc;w*uSpt=eGdSzYhF)$9C%;IAFZz&%OU2fBc+>zfQOA@aO&I zkb3>Eeg7KBD>V4pKi+Y0>I1-kdjGF;+}!~H@Dc#vX8miQK?(pAY5@Qw>tFkJ=>mX= z4gj>?-FgX#@^Ov`v~h7L1Dv89T%sIXod6WP)jS-3ZvVV24o)s^o^8B*`~rgD0resP zCkGc7CpQ-l4>zc=95=zw0d7$qv7Nf++r%Bt^D1AF(7Tm>pHJmTb(^H)JF2R_XK*CH zfRywO8CkVm>bv)77#Qw1GCpw7;;5ySwTUGr5#S|ydHY=O_45z78WMUf41N7Z zRCG*iTzo=eM&|9T?3_RDza;xbE>Tb}PHt{4Zr-1Aad3u# zjZ2i9XQ%EqF>?pr^H;=`^=|P=97(@l-Nvt???{#O41OmdrD{M@qx}@^Ps#r81dIHC zlI*_(`2F*O`rhnY&SU}?y00hy1sfEbP~famfSP=Hs&kxtIA?wLVH&PuYL;Mya$fXHXKfgU`J`Uu57 zQOuU$Wl46hu_o8SZ&~2hLIIpp2x9FqI4T!gfV2g`6iv2(_WOkI2l2nJ_4j@KtpmSx z;I|I^)`8zT@LLCd>%eav_^kuKb>O!S{MLcrI`CTue(S(*9r&#SzjffZ4*b@E-#YMH z2Y&0oZyorp1OM}N;QJP^1#pw7xMj)Nsz}-SET+9_IeT|bCqmy{;R0*H2zoB?kR|?N zscSVfw}xaSq|hTj$BsD3VJUHvcWV%=ize;{ez)KE;D3+~aBaQYWR)XD8F+Il_SPfx z!!9H0mm5}DTvH#;o-t0{eLne;?==&EC&>9yb+NzV&Y_jM!`NgX-csLa;qsz=ksmwgub@boY?otS0ynqyvB8VpFSwnL!~ zzp2Yky5xCFSN2d|#zQ}V6s(_tlL>hNcNnac~ro>beF1FIdhX!%4r?M0L-aTjx%it?=&Wcpd2(J|ZszBO7?B`AV7Vx=(f!hK&M+w@5 z*{4wPuLFwcOpDDt6OrRIv=_XIhKk?c*t;WEntAY*^^tVcMa3O&q%nx~Z)VWRPq$zJ zRTBf`u!Lod40Q`2E2dX1<`h{)z8#viHHcKzJK&^ntti{Hy)~jLh2RaHh?}iyn~7%g zjIwk{3I}y0?23tJJ_PCn$>-kAk-pY9#(j35-V@zx9FmbS#$1wgdqBd$^3Qg?pmQL3 zMxa&L#WesYX>#g3Dg3OJRqVuSsY~>b@`}&#waW*LzjWV7qzy1G;m5ZCcH&Ij7C_Yw zoW@+Gj*(_MKO8W?$4~DVHi7#{QRLn3-8J%;xf1IwR(8?zP$lFAPOJdhqFc;Rrw}@E z+t4?lwO`zL6AV*KhqSWSmX}d#iE6#7ca?8tTm|KBj5N7Qf+Uzq*U+jWz6Rb5JMzAz zBj|bn+gtCuqXE1=?rdf9!ztN?#aR?XZ3|%iLFj;f3&m|7@<7ZO01lQC9P5PSEg%p% z@sRW>0o5W$K_$rUO{w@&{`ji@m5?$mtp#~rkDfp-{Efz09bz8+8l#x(j^o9cFrpry zquiTa6*Ujp-BNv9y(es^wa-agdR1=$LmSp!0Qh4Csi>|L^`rGqX_o97x^!a$P0Mlm z2z@|_X|Y=|aA!RCg~EQu{}PP~qoIvd8x2v^HAl_KPAMof7YgrrCTBB*J`CPC1UU z0gV1=eO?pltKb$OEdO+52W&!1)xP zmKK4TkUFWyTJdt#p#a^G45K>2iSV8 zj{3^WlmaEO<;b>U5!ezmjx7+y6hlXTH<)8Y(fPfD9*s4JZnjuY^$lM4JNzO0MO-(b z0lqXb8>HWHqkc6tWYZZUg|vS7WoANuSJBWL9U!Y=uam^A{#_nbuHMQjGHPj<3Z?MI z`cMn-Eutke6DB??Aq}a5Ntlhm>tsRIIa zA5ySAV3qSsl6!wvDstD6zDmR+?#JCXHS?)s%W+H{bbqHTE(W7bGdenAc+=Utv93Py zi<@g{8p%PrNj*Z7yvoodfSMQxtKC~dJF$ASUQugMCuFYi{g9eFVV8fWFjpVonM?`{++jwa=q-LV~-U-p{6|S#Ns|9u@8~(qh3hWbGNNXHI<8hC5Izldgn@ zURAL-S?~7|?^UfiptS<(oTsh2>U>KcTR>q8*mahMV7Db~0VTSK2iR9LYXJma*b;mY zw-G4|EVim+LK%msXTEfh8AwnR2z^LTF;wZ-x*d3zle8R!~bG_ni-qS zLzumr#Vw*Y!OM?tRx8;U2r_Y&jZP~*`(^levADZk3N5X#{770=9WVD4WamIqi%vOO zrORy~%ESUP@zs9Y8o7M|wgL}Qu~cRYl}5no;XLe7TrIK_F7Cq6e>o#s7lb~`z zEpg6d=(bgQ^r^{pyV6B2zP1>8t5DANL);& zEgvb&@-%;Q^A6Jw+d~k>`4z-?Xg9^R(^bf_6Sh}N76vDKBw0BP)mXBO?@2|QIRRyj zj}eZ1z>_qNu`S^5?)7X>$0`v=$1tkMYSf$#9Ms=bdDKJw4>#8-el^{mZ+@yex#2o2s&hU>vQG#mi_E8JAjDB^=PWPy79~u|`4p3NC%+-p0zI zS1%RkpvOM3)R`XG*ZBu&3USdGC{5FHE${Wwe5% zm6k(~7k;QGU81xMhsuTf?^Vh;pmJ0MmAnrT{TT0c{^|V)Vjp~|hb0z8lI|o#BWn+j zGptFsL_Hf%SWVcx)HtTqFO z{}Jx?wICKM-zviLx3_}Fm}(8P9GlJK&*sTQx0)B=atI1WOBZhn<8)`J&hQvEH^Y_b z=swOeeHRoWsDSFAyWOQXIA}?cKd$UO7iN>jZQn1DEdXd|zRyGk;M#;?b5L3yyZJNp zhwvtV{r=PQQV6nG7)E(BAEPgwgwT_;i<#_!-07H8wTnZ)7=1Y7X@Ua;LlR$7?mM!x<{L zms>zmE~KkKjO98O68Hhq?t3)H-c@HOnrHd-H<4qJYH6H{vYlP`(2h=5*JVL$BpkQox6?rM}2X!SI&3FE&e+%*zG#Bi483^raWwEI+s$;$u2HMiZwoJ7i&z*h4I9 zs&CwVrfF6oI@4u%S-BUrcY1WqrRYqCZToH+b;HfmxHj++n(Jck*aDuHf$pBT7IAoh zLRfa9%FzQOyV&~}rP{KeP6>@RBuF$OPHEhg7I~Sv5J7tpafWu6b~dMNHH9G9YKk^D z38m&B z5I45t$#)Ds*G&eF=kogo>R$GFCThc_f&K2}z5|z60|pTF8~;G9lHWm@xEpDoQcZVa zglMrc2_qw79eU83hTmcpJe1hAYVo+{DZCu zzN|UZM|$AbCh%HvpOLpvX7cRV3U564&HS0_f`p~%w@i{p*2PM`8Lv++ubNgAi8gPo zKbpxAei>!RLxl#5W9=kOZUIt^T(-86)zEBsURhJ##giR7rrs`3n9b#~Ps3)5pieQ- z8jt!148lu9j%-dui=F;t2fav^+)g{)OTVsqvdA(-=-qy<^RDh{7O)LNW}xjyZ1)x* z=|0wBl(Yrhw8Sr(fuj9AeEQY#(=6Eh@4(rgp^o)b2~_n!Qa$Gw&VjLbo1xj;ZJR|; 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literal 0
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diff --git a/exps/.gitignore b/exps/.gitignore
deleted file mode 100644
index c96a04f..0000000
--- a/exps/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*
-!.gitignore
\ No newline at end of file
diff --git a/probts/data/data_manager.py b/probts/data/data_manager.py
index 8d47068..6f61a9d 100644
--- a/probts/data/data_manager.py
+++ b/probts/data/data_manager.py
@@ -4,15 +4,15 @@
 
 from gluonts.dataset.common import ListDataset
 from gluonts.dataset.field_names import FieldName
-from gluonts.dataset.repository import dataset_names, datasets
 from gluonts.dataset.multivariate_grouper import MultivariateGrouper
 
 from .ltsf_datasets import get_LTSF_info, get_LTSF_borders, get_LTSF_Dataset
-from .stsf_datasets import GluonTSDataset
 from .time_features import get_lags
-
-from probts.utils import StandardScaler, TemporalScaler, IdentityScaler
-import sys
+from .data_wrapper import ProbTSBatchData
+from .multi_horizon_dataset import MultiHorizonDataset
+from probts.utils import StandardScaler, TemporalScaler, IdentityScaler, convert_to_list
+from typing import Union
+import math
 
 MULTI_VARIATE_DATASETS = [
     'exchange_rate_nips',
@@ -24,51 +24,6 @@
     'wiki2000_nips'
 ]
 
-
-class ProbTSBatchData:
-    input_names_ = [
-        'target_dimension_indicator',
-        'past_time_feat',
-        'past_target_cdf',
-        'past_observed_values',
-        'past_is_pad',
-        'future_time_feat',
-        'future_target_cdf',
-        'future_observed_values',
-    ]
-    
-    def __init__(self, data_dict, device):
-        self.__dict__.update(data_dict)
-        if len(self.__dict__['past_target_cdf'].shape) == 2:
-            self.expand_dim()
-        self.set_device(device)
-        self.fill_inputs()
-        self.process_pad()
-
-    def fill_inputs(self):
-        for input in self.input_names_:
-            if input not in self.__dict__:
-                self.__dict__[input] = None
-
-    def set_device(self, device):
-        for k, v in self.__dict__.items():
-            if v is not None:
-                v.to(device)
-        self.device = device
-
-    def expand_dim(self):
-        self.__dict__["target_dimension_indicator"] = self.__dict__["target_dimension_indicator"][:, :1]
-        for input in ['past_target_cdf','past_observed_values','future_target_cdf','future_observed_values']:
-            self.__dict__[input] = self.__dict__[input].unsqueeze(-1)
-
-    def process_pad(self):
-        if self.__dict__['past_is_pad'] is not None:
-            self.__dict__["past_observed_values"] = torch.min(
-                self.__dict__["past_observed_values"],
-                1 - self.__dict__["past_is_pad"].unsqueeze(-1)
-            )
-
-
 class DataManager:
 
     def __init__(
@@ -76,27 +31,30 @@ def __init__(
         dataset,
         path: str = './datasets',
         history_length: int = None,
-        context_length: int = None,
-        prediction_length: int = None,
-        test_rolling_length: int = 96,
+        context_length: Union[list,int,str] = None,
+        prediction_length: Union[list,int,str] = None,
+        train_ctx_len_list: Union[list,int,str] = None,
+        train_pred_len_list: Union[list,int,str] = None,
+        val_pred_len_list: Union[list,int,str] = None,
+        val_ctx_len_list: Union[list,int,str] = None,
+        test_rolling_length: int = None,
         split_val: bool = True,
         scaler: str = 'none',
         test_sampling: str = 'ctx', # ['arrange', 'ctx', 'pred']
-        context_length_factor: int = 1,
         timeenc: int = 1,
         var_specific_norm: bool = True,
-        data_path: str = None,
-        freq: str = None,
-        multivariate: bool = True,
+        continous_sample: bool = False,
     ):
         self.dataset = dataset
-        self.test_rolling_length = test_rolling_length
-        self.global_mean = None
-        self.multivariate = multivariate 
+        self.multivariate = True 
         self.split_val = split_val
         self.test_sampling = test_sampling
         self.timeenc = timeenc
         self.var_specific_norm = var_specific_norm
+        self.continous_sample = continous_sample
+        self.test_rolling_length = test_rolling_length
+        
+        self.test_rolling_dict = {'h': 24, 'd': 7, 'b':5, 'w':4, 'min': 60}
 
         if scaler == 'standard':
             self.scaler = StandardScaler(var_specific=self.var_specific_norm)
@@ -104,55 +62,59 @@ def __init__(
             self.scaler = TemporalScaler()
         else:
             self.scaler = IdentityScaler()
+
+        # multi-horizon training
+        self.pred_len_list = convert_to_list(train_pred_len_list)
+        if self.pred_len_list is None:
+            self.pred_len_list = convert_to_list(prediction_length)
+        
+        self.ctx_len_list = convert_to_list(train_ctx_len_list)
+        if self.ctx_len_list is None:
+            self.ctx_len_list = convert_to_list(context_length)
+            
+            
+        self.val_pred_len_list = convert_to_list(val_pred_len_list)
+        if self.val_pred_len_list is None:
+            self.val_pred_len_list = self.pred_len_list
+        
+        self.val_ctx_len_list = convert_to_list(val_ctx_len_list)
+        if self.val_ctx_len_list is None:
+            self.val_ctx_len_list = self.ctx_len_list
     
-        if dataset in dataset_names:
-            """
-            Use gluonts to load short-term datasets.
-            """
-            print("Loading Short-term Datasets: {dataset}".format(dataset=dataset))
-            self.dataset_raw = datasets.get_dataset(dataset, path=Path(path), regenerate=False)
 
-            # Meta parameters
-            self.target_dim = int(self.dataset_raw.metadata.feat_static_cat[0].cardinality)
-            self.freq = self.dataset_raw.metadata.freq.upper()
-            self.lags_list = get_lags(self.freq)
-            self.prediction_length = self.dataset_raw.metadata.prediction_length \
-                if prediction_length is None else prediction_length
-            self.context_length = self.dataset_raw.metadata.prediction_length * context_length_factor \
-                if context_length is None else context_length
-            self.history_length = (self.context_length + max(self.lags_list)) \
-                if history_length is None else history_length
+        # Load datasets.
+        print("Loading Long-term Datasets: {dataset}".format(dataset=dataset))
 
-            self.prepare_STSF_dataset(dataset)
-        else:
-            """
-            Load long-term datasets.
-            """
-            print("Loading Long-term Datasets: {dataset}".format(dataset=dataset))
+        if context_length is None or prediction_length is None:
+            raise ValueError("The context_length or prediction_length is not assigned.")
+
+        data_path, self.freq = get_LTSF_info(dataset)
+        self.dataset_raw, self.data_stamp, self.target_dim, data_size = get_LTSF_Dataset(path, data_path,freq=self.freq,timeenc=self.timeenc)
+        self.border_begin, self.border_end = get_LTSF_borders(dataset, data_size)
 
-            if context_length is None or prediction_length is None:
-                raise ValueError("The context_length or prediction_length is not assigned.")
+        assert data_size >= self.border_end[2], print("\n The end index larger then data size!")
 
-            data_path, self.freq = get_LTSF_info(dataset, data_path=data_path, freq=freq)
-            self.dataset_raw, self.data_stamp, self.target_dim, data_size = get_LTSF_Dataset(path, data_path,freq=self.freq,timeenc=self.timeenc, multivariate=self.multivariate)
-            self.border_begin, self.border_end = get_LTSF_borders(dataset, data_size)
+        # Meta parameters
+        self.lags_list = get_lags(self.freq)
+        self.prediction_length = convert_to_list(prediction_length)
+        self.context_length = convert_to_list(context_length)
+        self.history_length = (max(self.context_length) + max(self.lags_list)) \
+            if history_length is None else history_length
             
-            if not self.multivariate:
-                self.target_dim = 1
-                raise NotImplementedError("Support for customized univariate dataset is still work in progress.")
-                
-            assert data_size >= self.border_end[2], print("\n The end index larger then data size!")
+        # define the test_rolling_length
+        if self.test_rolling_length is None:
+            if self.freq.lower() in self.test_rolling_dict:
+                self.test_rolling_length = self.test_rolling_dict[self.freq.lower()]
+            else:
+                self.test_rolling_length = 24
 
-            # Meta parameters
-            self.lags_list = get_lags(self.freq)
-            self.prediction_length = prediction_length
-            self.context_length = context_length
-            self.history_length = (self.context_length + max(self.lags_list)) \
-                if history_length is None else history_length
+        self.prepare_LTSF_dataset()
 
-            self.prepare_LTSF_dataset()
+        print(f"test context_length: {self.context_length}, test prediction_length: {self.prediction_length}")
+        print(f"training context lengths: {self.ctx_len_list}, training prediction lengths: {self.pred_len_list}")
+        print(f"Sampling T from [{min(self.pred_len_list)},  {max(self.pred_len_list)}], Continuous: {self.continous_sample}")
+        print(f"test_rolling_length: {self.test_rolling_length}")
 
-        print(f"context_length: {self.context_length}, prediction_length: {self.prediction_length}")
         if scaler == 'standard':
             print(f"variate-specific normalization: {self.var_specific_norm}")
 
@@ -174,22 +136,34 @@ def prepare_LTSF_dataset(self):
         group_val_set = test_grouper(val_set)
         group_test_set = test_grouper(test_set)
         
-        group_val_set = self.get_rolling_test(group_val_set, self.border_begin[1], self.border_end[1], rolling_length=self.test_rolling_length)
-        group_test_set = self.get_rolling_test(group_test_set, self.border_begin[2], self.border_end[2], rolling_length=self.test_rolling_length)
-        
-        stsf_dataset_loader = GluonTSDataset(
-            ProbTSBatchData.input_names_, 
-            self.history_length,
+        dataset_loader = MultiHorizonDataset(ProbTSBatchData.input_names_, 
+            self.context_length,
             self.prediction_length,
             self.freq,
-            self.multivariate
-        )
+            self.ctx_len_list,
+            self.pred_len_list,
+            self.val_ctx_len_list,
+            self.val_pred_len_list,
+            self.multivariate,
+            self.continous_sample)
+        
+        self.train_iter_dataset = dataset_loader.get_iter_dataset(group_train_set, mode='train', data_stamp=self.data_stamp[: self.border_end[0]])
         
-        self.train_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_train_set, mode='train', data_stamp=self.data_stamp[: self.border_end[0]])
-        self.val_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_val_set, mode='val', data_stamp=self.data_stamp[: self.border_end[1]])
-        self.test_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_test_set, mode='test', data_stamp=self.data_stamp[: self.border_end[2]])
-        self.time_feat_dim = stsf_dataset_loader.time_feat_dim
-        self.global_mean = torch.mean(torch.tensor(group_train_set[0]['target']), dim=-1)
+        # for multi-horizon evaluation
+        self.val_iter_dataset = {}
+        self.test_iter_dataset = {}
+
+        # validation set
+        for pred_len in self.val_pred_len_list:
+            local_group_val_set = self.get_rolling_test('val', group_val_set, self.border_begin[1], self.border_end[1], rolling_length=self.test_rolling_length, pred_len=pred_len)
+            self.val_iter_dataset[str(pred_len)] = dataset_loader.get_iter_dataset(local_group_val_set, mode='val', data_stamp=self.data_stamp[: self.border_end[1]], pred_len=[pred_len])
+            
+        # testing set
+        for pred_len in self.prediction_length:
+            local_group_test_set = self.get_rolling_test('test', group_test_set, self.border_begin[2], self.border_end[2], rolling_length=self.test_rolling_length, pred_len=pred_len)
+            self.test_iter_dataset[str(pred_len)] = dataset_loader.get_iter_dataset(local_group_test_set, mode='test', data_stamp=self.data_stamp[: self.border_end[2]], pred_len=[pred_len])
+        
+        self.time_feat_dim = dataset_loader.time_feat_dim
 
     def df_to_mvds(self, df, freq='H'):
         datasets = []
@@ -199,18 +173,15 @@ def df_to_mvds(self, df, freq='H'):
         dataset = ListDataset(datasets,freq=freq)
         return dataset
 
-    def get_rolling_test(self, test_set, border_begin_idx, border_end_idx, rolling_length):
-        if (border_end_idx - border_begin_idx - self.prediction_length) < 0:
-            raise ValueError("The time steps in validation / testing set is less than prediction length.")
-        
-        num_test_dates = int(((border_end_idx - border_begin_idx - self.prediction_length) / rolling_length)) + 1
-        print("num_test_dates: ", num_test_dates)
+    def get_rolling_test(self, stage, test_set, border_begin_idx, border_end_idx, rolling_length, pred_len):
+        num_test_dates = math.ceil(((border_end_idx - border_begin_idx - pred_len) / rolling_length))
+        print(f"{stage}  pred_len: {pred_len} : num_test_dates: {num_test_dates}")
 
         test_set = next(iter(test_set))
         rolling_test_seq_list = list()
         for i in range(num_test_dates):
             rolling_test_seq = deepcopy(test_set)
-            rolling_end = border_begin_idx + self.prediction_length + i * rolling_length
+            rolling_end = border_begin_idx + pred_len + i * rolling_length
             rolling_test_seq[FieldName.TARGET] = rolling_test_seq[FieldName.TARGET][:, :rolling_end]
             rolling_test_seq_list.append(rolling_test_seq)
 
@@ -219,100 +190,4 @@ def get_rolling_test(self, test_set, border_begin_idx, border_end_idx, rolling_l
         )
         return rolling_test_set
 
-    def split_train_val(self, train_set):
-        trunc_train_list = []
-        val_set_list = []
-        univariate = False
-
-        for train_seq in iter(train_set):
-            # truncate train set
-            offset = self.num_test_dates * self.prediction_length
-            trunc_train_seq = deepcopy(train_seq)
-
-            if len(train_seq[FieldName.TARGET].shape) == 1:
-                trunc_train_len = train_seq[FieldName.TARGET].shape[0] - offset
-                trunc_train_seq[FieldName.TARGET] = train_seq[FieldName.TARGET][:trunc_train_len]
-                univariate = True
-            elif len(train_seq[FieldName.TARGET].shape) == 2:
-                trunc_train_len = train_seq[FieldName.TARGET].shape[1] - offset
-                trunc_train_seq[FieldName.TARGET] = train_seq[FieldName.TARGET][:, :trunc_train_len]
-            else:
-                raise ValueError(f"Invalid Data Shape: {str(len(train_seq[FieldName.TARGET].shape))}")
-
-            trunc_train_list.append(trunc_train_seq)
-
-            # construct val set by rolling
-            for i in range(self.num_test_dates):
-                val_seq = deepcopy(train_seq)
-                rolling_len = trunc_train_len + self.prediction_length * (i+1)
-                if univariate:
-                    val_seq[FieldName.TARGET] = val_seq[FieldName.TARGET][trunc_train_len + self.prediction_length * (i-1) - self.context_length : rolling_len]
-                else:
-                    val_seq[FieldName.TARGET] = val_seq[FieldName.TARGET][:, :rolling_len]
-                
-                val_set_list.append(val_seq)
-
-        trunc_train_set = ListDataset(
-            trunc_train_list, freq=self.freq, one_dim_target=univariate
-        )
-
-        val_set = ListDataset(
-            val_set_list, freq=self.freq, one_dim_target=univariate
-        )
-        
-        return trunc_train_set, val_set
-
-    def truncate_test(self, test_set):
-        trunc_test_list = []
-        for test_seq in iter(test_set):
-            # truncate train set
-            trunc_test_seq = deepcopy(test_seq)
-
-            trunc_test_seq[FieldName.TARGET] = trunc_test_seq[FieldName.TARGET][- ( self.prediction_length * 2 + self.context_length):]
-
-            trunc_test_list.append(trunc_test_seq)
-
-        trunc_test_set = ListDataset(
-            trunc_test_list, freq=self.freq, one_dim_target=True
-        )
-
-        return trunc_test_set
-    
-    def prepare_STSF_dataset(self, dataset: str):
-        if dataset in MULTI_VARIATE_DATASETS:
-            self.num_test_dates = int(len(self.dataset_raw.test)/len(self.dataset_raw.train))
-
-            train_grouper = MultivariateGrouper(max_target_dim=int(self.target_dim))
-            test_grouper = MultivariateGrouper(
-                num_test_dates=self.num_test_dates, 
-                max_target_dim=int(self.target_dim)
-            )
-            train_set = train_grouper(self.dataset_raw.train)
-            test_set = test_grouper(self.dataset_raw.test)
-            self.scaler.fit(torch.tensor(train_set[0]['target'].transpose(1, 0)))
-            self.global_mean = torch.mean(torch.tensor(train_set[0]['target']), dim=-1)
-        else:
-            self.target_dim = 1
-            self.multivariate = False
-            self.num_test_dates = 1
-            train_set = self.dataset_raw.train
-            test_set = self.dataset_raw.test
-            test_set = self.truncate_test(test_set)
-            
-        if self.split_val:
-            train_set, val_set = self.split_train_val(train_set)
-        else:
-            val_set = test_set
-
-        stsf_dataset_loader = GluonTSDataset(
-            ProbTSBatchData.input_names_, 
-            self.history_length,
-            self.prediction_length,
-            self.freq,
-            self.multivariate
-        )
 
-        self.train_iter_dataset = stsf_dataset_loader.get_iter_dataset(train_set, mode='train')
-        self.val_iter_dataset = stsf_dataset_loader.get_iter_dataset(val_set, mode='val')
-        self.test_iter_dataset = stsf_dataset_loader.get_iter_dataset(test_set, mode='test')
-        self.time_feat_dim = stsf_dataset_loader.time_feat_dim
diff --git a/probts/data/data_module.py b/probts/data/data_module.py
index e073b38..3c19b8c 100644
--- a/probts/data/data_module.py
+++ b/probts/data/data_module.py
@@ -1,7 +1,10 @@
 import os
+import torch
 import lightning.pytorch as pl
 from torch.utils.data import DataLoader
+from lightning.pytorch.utilities.combined_loader import CombinedLoader
 from .data_manager import DataManager
+from .data_wrapper import ProbTSBatchData
 
 class ProbTSDataModule(pl.LightningDataModule):
     r"""
@@ -43,4 +46,100 @@ def test_dataloader(self):
         return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=1)
 
     def predict_dataloader(self):
-        return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=1)
\ No newline at end of file
+        return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=1)
+    
+    
+class ElasTSTDataModule(pl.LightningDataModule):
+    r"""
+        DataModule for probablistic time series datasets.
+    """
+    def __init__(
+        self,
+        data_manager: DataManager,
+        batch_size: int = 64,
+        test_batch_size: int = 8,
+        num_workers: int = 8
+    ):
+        super().__init__()
+        self.data_manager = data_manager
+        self.batch_size = batch_size
+        self.test_batch_size = test_batch_size
+        self.num_workers = num_workers
+        self.save_hyperparameters()
+
+    def setup(self, stage: str):
+        self.dataset_train = self.data_manager.train_iter_dataset
+        self.dataset_val = self.data_manager.val_iter_dataset
+        self.dataset_test = self.data_manager.test_iter_dataset
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.dataset_train,
+            batch_size=self.batch_size,
+            num_workers=0,
+            pin_memory=True,
+            collate_fn=self.train_collate_fn
+        )
+
+    def val_dataloader(self):
+        return self.combine_dataloader(self.dataset_val)
+
+    def test_dataloader(self):
+        return self.combine_dataloader(self.dataset_test)
+
+    def predict_dataloader(self):
+        return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=0)
+    
+    def combine_dataloader(self, dataset_dict):
+        dataloader_dict = {}
+        for hor in dataset_dict:
+            dataloader_dict[hor] = DataLoader(dataset_dict[hor], batch_size=self.test_batch_size, num_workers=0, persistent_workers=False,)
+        
+        combined_loader = CombinedLoader(dataloader_dict, mode="sequential")
+        return combined_loader
+    
+    def train_collate_fn(self, batch):
+        '''
+        Training with varied horizons is achieved by padding horizons in training phase.
+        The look-back window for each sample can different within a batch.
+        '''
+        
+        past_len_list = [len(x['past_target_cdf']) for x in batch]
+        future_len_list = [len(x['future_target_cdf']) for x in batch]
+        
+        max_past_length = max(past_len_list)
+        max_future_length = max(future_len_list)
+        B = len(batch)
+        batch_dict = {}
+        batch_dict['context_length'] = []
+        batch_dict['prediction_length'] = []
+        batch_dict['target_dimension_indicator'] = []
+        
+        for idx in range(len(batch)):
+            local_past_len = len(batch[idx]['past_target_cdf'])
+            local_future_len = len(batch[idx]['future_target_cdf'])
+                
+            for input in ProbTSBatchData.input_names_:
+                K = batch[0][input].shape[-1]
+                if input in ['past_target_cdf','past_observed_values','past_time_feat','past_is_pad']:
+                    if input not in batch_dict and input in ['past_target_cdf','past_observed_values','past_time_feat']:
+                        batch_dict[input] = torch.zeros([B, max_past_length, K])
+                    if input not in batch_dict and input in ['past_is_pad']:
+                        batch_dict[input] = torch.zeros([B, max_past_length])
+                        
+                    batch_dict[input][idx][-local_past_len:] = torch.tensor(batch[idx][input])[:local_past_len]
+
+                elif input in ['future_target_cdf','future_observed_values','future_time_feat']:
+                    if input not in batch_dict:
+                        batch_dict[input] = torch.zeros([B, max_future_length, K])
+                    batch_dict[input][idx][:local_future_len] = torch.tensor(batch[idx][input])[:local_future_len]
+
+            batch_dict['target_dimension_indicator'].append(batch[idx]['target_dimension_indicator'])
+            batch_dict['context_length'].append(local_past_len)
+            batch_dict['prediction_length'].append(local_future_len)
+            
+        batch_dict['target_dimension_indicator'] = torch.tensor(batch_dict['target_dimension_indicator'])
+        
+        batch_dict['max_context_length'] = max_past_length
+        batch_dict['max_prediction_length'] = max_future_length
+        return batch_dict
\ No newline at end of file
diff --git a/probts/data/data_wrapper.py b/probts/data/data_wrapper.py
new file mode 100644
index 0000000..9ecdf2d
--- /dev/null
+++ b/probts/data/data_wrapper.py
@@ -0,0 +1,60 @@
+import torch
+
+class ProbTSBatchData:
+    input_names_ = [
+        'target_dimension_indicator',
+        'past_time_feat',
+        'past_target_cdf',
+        'past_observed_values',
+        'past_is_pad',
+        'future_time_feat',
+        'future_target_cdf',
+        'future_observed_values',
+    ]
+    
+    def __init__(self, data_dict, device):
+        self.__dict__.update(data_dict)
+        if 'context_length' in data_dict:
+            self.__dict__['context_length'] = data_dict['context_length']
+        if 'prediction_length' in data_dict:
+            self.__dict__['prediction_length'] = data_dict['prediction_length']
+            
+        if 'max_context_length' in data_dict:
+            self.__dict__['max_context_length'] = data_dict['max_context_length']
+        else:
+            self.__dict__['max_context_length'] = None
+            
+        if 'max_prediction_length' in data_dict:
+            self.__dict__['max_prediction_length'] = data_dict['max_prediction_length']
+        else:
+            self.__dict__['max_prediction_length'] = None
+            
+        if len(self.__dict__['past_target_cdf'].shape) == 2:
+            self.expand_dim()
+        self.set_device(device)
+        self.fill_inputs()
+        self.process_pad()
+
+    def fill_inputs(self):
+        for input in self.input_names_:
+            if input not in self.__dict__:
+                self.__dict__[input] = None
+
+    def set_device(self, device):
+        for k, v in self.__dict__.items():
+            if v is not None and torch.is_tensor(v):
+                v.to(device)
+        self.device = device
+
+    def expand_dim(self):
+        self.__dict__["target_dimension_indicator"] = self.__dict__["target_dimension_indicator"][:, :1]
+        for input in ['past_target_cdf','past_observed_values','future_target_cdf','future_observed_values']:
+            self.__dict__[input] = self.__dict__[input].unsqueeze(-1)
+
+    def process_pad(self):
+        if self.__dict__['past_is_pad'] is not None:
+            self.__dict__["past_observed_values"] = torch.min(
+                self.__dict__["past_observed_values"],
+                1 - self.__dict__["past_is_pad"].unsqueeze(-1)
+            )
+
diff --git a/probts/data/ltsf_datasets.py b/probts/data/ltsf_datasets.py
index c0ffad1..c9ceca7 100644
--- a/probts/data/ltsf_datasets.py
+++ b/probts/data/ltsf_datasets.py
@@ -12,13 +12,13 @@
 from .monash_datasets import convert_monash_data_to_dataframe, monash_format_convert
 import numpy as np
 
-def get_LTSF_info(dataset, data_path=None, freq=None):
+def get_LTSF_info(dataset):
     if dataset == 'etth1' or dataset == 'etth2':
         if dataset == 'etth1':
             data_path = 'ETT-small/ETTh1.csv'
         else:
             data_path = 'ETT-small/ETTh2.csv'
-        freq = 'H'
+        freq = 'h'
     elif dataset == 'ettm1' or dataset == 'ettm2':
         if dataset == 'ettm1':
             data_path = 'ETT-small/ETTm1.csv'
@@ -28,10 +28,10 @@ def get_LTSF_info(dataset, data_path=None, freq=None):
     elif dataset in ['traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf']:
         if dataset == 'traffic_ltsf':
             data_path = 'traffic/traffic.csv'
-            freq = 'H'
+            freq = 'h'
         elif dataset == 'electricity_ltsf':
             data_path = 'electricity/electricity.csv'
-            freq = 'H'
+            freq = 'h'
         elif dataset == 'exchange_ltsf':
             data_path = 'exchange_rate/exchange_rate.csv'
             freq = 'B'
@@ -43,15 +43,24 @@ def get_LTSF_info(dataset, data_path=None, freq=None):
             freq = 'min'
     elif dataset == 'caiso':
         data_path = 'caiso/caiso_20130101_20210630.csv'
-        freq = 'H'
+        freq = 'h'
     elif dataset == 'nordpool':
         data_path = 'nordpool/production.csv'
-        freq = 'H'
+        freq = 'h'
+    elif dataset == 'solar_hour':
+        data_path = 'monash/solar_10_minutes_dataset.tsf'
+        freq = 'h'
+    elif dataset == 'solar_10min':
+        data_path = 'monash/solar_10_minutes_dataset.tsf'
+        freq = 'min'
+    elif dataset == 'sunspot':
+        data_path = 'monash/sunspot_dataset_without_missing_values.tsf'
+        freq = 'D'
+    elif dataset == 'river_flow':
+        data_path = 'monash/saugeenday_dataset.tsf'
+        freq = 'D'
     else:
-        assert data_path is not None, f'Invalid dataset name: {dataset}! To use customizd datasets, the --data.data_manager.init_args.data_path should be assigned!'
-        assert freq is not None, 'To use customizd datasets, the --data.data_manager.init_args.freq should be assigned!'
-    # else:
-    #     raise ValueError(f"Invalid dataset name: {dataset}!")
+        raise ValueError(f"Invalid dataset name: {dataset}!")
     return data_path, freq
 
 def get_LTSF_borders(dataset, data_size):
@@ -69,7 +78,7 @@ def get_LTSF_borders(dataset, data_size):
         border_end = [num_train, num_train + num_vali, data_size]
     return border_begin, border_end
 
-def get_LTSF_Dataset(root_path, data_path,freq='h', timeenc=1, multivariate=True):
+def get_LTSF_Dataset(root_path, data_path, freq='h', timeenc=1, multivariate=True):
     if 'caiso' in data_path:
         data = pd.read_csv(root_path + data_path)
         data['Date'] = data['Date'].astype('datetime64[ns]')
diff --git a/probts/data/multi_horizon_dataset.py b/probts/data/multi_horizon_dataset.py
new file mode 100644
index 0000000..86ab0ff
--- /dev/null
+++ b/probts/data/multi_horizon_dataset.py
@@ -0,0 +1,388 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from GluonTS
+# - Source: https://github.com/awslabs/gluonts
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+from torch.utils.data import IterableDataset
+from gluonts.env import env
+from gluonts.dataset.common import Dataset
+from gluonts.dataset.field_names import FieldName
+from gluonts.transform import (
+    SelectFields,
+    Transformation,
+    Chain,
+    ValidationSplitSampler,
+    ExpectedNumInstanceSampler,
+    RenameFields,
+    AsNumpyArray,
+    ExpandDimArray,
+    AddObservedValuesIndicator,
+    AddTimeFeatures,
+    VstackFeatures,
+    SetFieldIfNotPresent,
+    TargetDimIndicator,
+    InstanceSplitter
+)
+from .time_features import fourier_time_features_from_frequency, AddCustomizedTimeFeatures
+from .stsf_datasets import TransformedIterableDataset
+from typing import Union
+from gluonts.dataset.common import DataEntry
+from gluonts.transform import InstanceSampler
+from typing import Iterator, List, Optional, Tuple, Union
+import numpy as np
+from gluonts.zebras._util import pad_axis
+from gluonts.dataset.common import DataEntry
+import random
+from gluonts.transform._base import FlatMapTransformation
+import sys
+
+class MultiHorizonDataset():
+
+    def __init__(
+        self,
+        input_names: list,
+        context_length: Union[int, list],
+        prediction_length: Union[int, list],
+        freq: str,
+        ctx_range: Union[int, list]=None,
+        pred_range: Union[int, list]=None,
+        val_ctx_range: Union[int, list]=None,
+        val_pred_range: Union[int, list]=None,
+        multivariate: bool = True,
+        continous_sample: bool = False,
+    ):
+        super().__init__()
+        self.input_names_ = input_names
+        self.ctx_range = ctx_range
+        self.pred_range = pred_range
+        self.val_ctx_range = val_ctx_range
+        self.val_pred_range = val_pred_range
+        self.context_length = context_length
+        self.prediction_length=prediction_length
+        self.continous_sample = continous_sample
+        
+        self.freq = freq
+        if multivariate:
+            self.expected_ndim = 2
+        else:
+            self.expected_ndim = 1
+
+    def get_sampler(self):
+        # for training
+        train_min_past = min(self.ctx_range)
+        train_min_future = min(self.pred_range)
+        
+        # for validation
+        val_min_past = max(self.val_ctx_range)
+        val_min_future = max(self.val_pred_range)
+        
+        # for testing
+        if (type(self.context_length).__name__=='list'):
+            test_min_past = max(self.context_length)
+        else:
+            test_min_past=self.context_length
+        
+        if (type(self.pred_range).__name__=='list'):
+            test_min_future = max(self.prediction_length)
+        else:
+            test_min_future=self.prediction_length
+
+        self.train_sampler = ExpectedNumInstanceSampler(
+            num_instances=1.0,
+            min_past=train_min_past,
+            min_future=train_min_future,
+        )
+
+        self.val_sampler = ValidationSplitSampler(
+            min_past=val_min_past,
+            min_future=val_min_future,
+        )
+        
+        self.test_sampler = ValidationSplitSampler(
+            min_past=test_min_past,
+            min_future=test_min_future,
+        )
+
+        
+    def create_transformation(self, data_stamp=None, pred_len=None) -> Transformation:
+        if data_stamp is None:
+            if self.freq in ["M", "W", "D", "B", "H", "min", "T"]:
+                time_features = fourier_time_features_from_frequency(self.freq)
+            else:
+                time_features = fourier_time_features_from_frequency('D')
+            self.time_feat_dim = len(time_features) * 2
+            time_feature_func = AddTimeFeatures
+        else:
+            self.time_feat_dim = data_stamp.shape[-1]
+            time_features = data_stamp
+            time_feature_func = AddCustomizedTimeFeatures
+            
+        if pred_len is None:
+            pred_len = max(self.pred_range)
+        else:
+            pred_len = max(pred_len)
+            
+        return Chain(
+            [
+                AsNumpyArray(
+                    field=FieldName.TARGET,
+                    expected_ndim=self.expected_ndim,
+                ),
+                ExpandDimArray(
+                    field=FieldName.TARGET,
+                    axis=None,
+                ),
+                AddObservedValuesIndicator(
+                    target_field=FieldName.TARGET,
+                    output_field=FieldName.OBSERVED_VALUES,
+                ),
+                time_feature_func(
+                    start_field=FieldName.START,
+                    target_field=FieldName.TARGET,
+                    output_field=FieldName.FEAT_TIME,
+                    time_features=time_features,
+                    pred_length=pred_len,
+                ),
+                VstackFeatures(
+                    output_field=FieldName.FEAT_TIME,
+                    input_fields=[FieldName.FEAT_TIME],
+                ),
+                SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0]),
+                TargetDimIndicator(
+                    field_name="target_dimension_indicator",
+                    target_field=FieldName.TARGET,
+                ),
+                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
+            ]
+        )
+
+    def create_instance_splitter(self, mode: str, pred_len=None):
+        assert mode in ["train", "val", "test"]
+
+        self.get_sampler()
+        instance_sampler = {
+            "train": self.train_sampler,
+            "val": self.val_sampler,
+            "test": self.test_sampler,
+        }[mode]
+
+        if mode == "train":
+            past_length = self.ctx_range
+            future_length = self.pred_range
+        elif mode == 'val':
+            past_length = self.val_ctx_range
+            if pred_len is None:
+                future_length = self.val_pred_range
+            else:
+                future_length = pred_len
+        else:
+            past_length = self.context_length
+            if pred_len is None:
+                future_length = self.prediction_length
+            else:
+                future_length = pred_len
+            
+            
+        return MultiHorizonSplitter(
+            target_field=FieldName.TARGET,
+            is_pad_field=FieldName.IS_PAD,
+            start_field=FieldName.START,
+            forecast_start_field=FieldName.FORECAST_START,
+            instance_sampler=instance_sampler,
+            past_length=past_length,
+            future_length=future_length,
+            mode=mode,
+            continous_sample=self.continous_sample,
+            time_series_fields=[
+                FieldName.FEAT_TIME,
+                FieldName.OBSERVED_VALUES,
+            ],
+        ) + (
+            RenameFields(
+                {
+                    f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
+                    f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
+                }
+            )
+        )
+
+
+    def get_iter_dataset(self, dataset: Dataset, mode: str, data_stamp=None, pred_len=None) -> IterableDataset:
+        assert mode in ["train", "val", "test"]
+
+        transform = self.create_transformation(data_stamp, pred_len=pred_len)
+            
+            
+        if mode == 'train':
+            with env._let(max_idle_transforms=100):
+                instance_splitter = self.create_instance_splitter(mode)
+        else:
+            instance_splitter = self.create_instance_splitter(mode, pred_len=pred_len)
+
+
+        input_names = self.input_names_
+
+        iter_dataset = TransformedIterableDataset(
+            dataset,
+            transform=transform
+            + instance_splitter
+            + SelectFields(input_names),
+            is_train=True if mode == 'train' else False
+        )
+
+        return iter_dataset
+
+
+class MultiHorizonSplitter(FlatMapTransformation):
+    """
+    Split instances from a dataset, by slicing the target and other time series
+    fields at points in time selected by the specified sampler. The assumption
+    is that all time series fields start at the same time point.
+
+    It is assumed that time axis is always the last axis.
+
+    The ``target_field`` and each field in ``time_series_fields`` are removed and
+    replaced by two new fields, with prefix `past_` and `future_` respectively.
+
+    A ``past_is_pad`` is also added, that indicates whether values at a given
+    time point are padding or not.
+
+    Parameters
+    ----------
+
+    target_field
+        field containing the target
+    is_pad_field
+        output field indicating whether padding happened
+    start_field
+        field containing the start date of the time series
+    forecast_start_field
+        output field that will contain the time point where the forecast starts
+    instance_sampler
+        instance sampler that provides sampling indices given a time series
+    past_length
+        length of the target seen before making prediction
+    future_length
+        length of the target that must be predicted
+    lead_time
+        gap between the past and future windows (default: 0)
+    output_NTC
+        whether to have time series output in (time, dimension) or in
+        (dimension, time) layout (default: True)
+    time_series_fields
+        fields that contains time series, they are split in the same interval
+        as the target (default: None)
+    dummy_value
+        Value to use for padding. (default: 0.0)
+    """
+
+    # @validated()
+    def __init__(
+        self,
+        target_field: str,
+        is_pad_field: str,
+        start_field: str,
+        forecast_start_field: str,
+        instance_sampler: InstanceSampler,
+        past_length: Union[int, list],
+        future_length: Union[int, list],
+        mode: str,
+        lead_time: int = 0,
+        output_NTC: bool = True,
+        time_series_fields: List[str] = [],
+        dummy_value: float = 0.0,
+        continous_sample: bool = False,
+    ) -> None:
+        super().__init__()
+
+        # assert future_length > 0, "The value of `future_length` should be > 0"
+
+        self.instance_sampler = instance_sampler
+        self.past_length = past_length
+        self.future_length = future_length
+        self.continous_sample = continous_sample
+        
+        self.lead_time = lead_time
+        self.output_NTC = output_NTC
+        self.ts_fields = time_series_fields
+        self.target_field = target_field
+        self.is_pad_field = is_pad_field
+        self.start_field = start_field
+        self.forecast_start_field = forecast_start_field
+        self.dummy_value = dummy_value
+        self.mode = mode
+
+    def _past(self, col_name):
+        return f"past_{col_name}"
+
+    def _future(self, col_name):
+        return f"future_{col_name}"
+
+    def _split_array(
+        self, array: np.ndarray, idx: int, past_length: int, future_length: int
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        if idx >= past_length:
+            past_piece = array[..., idx - past_length : idx]
+        else:
+            past_piece = pad_axis(
+                array[..., :idx],
+                axis=-1,
+                left=past_length - idx,
+                value=self.dummy_value,
+            )
+
+        future_start = idx + self.lead_time
+        future_slice = slice(future_start, future_start + future_length)
+        future_piece = array[..., future_slice]
+
+        return past_piece, future_piece
+
+    def _split_instance(self, entry: DataEntry, idx: int, is_train) -> DataEntry:
+        slice_cols = self.ts_fields + [self.target_field]
+        dtype = entry[self.target_field].dtype
+        entry = entry.copy()
+        
+        if is_train:
+            if self.continous_sample:
+                past_len = random.randint(min(self.past_length), max(self.past_length))
+                pred_len = random.randint(min(self.future_length), max(self.future_length))
+            else:
+                past_len = random.choice(self.past_length) 
+                pred_len = random.choice(self.future_length) 
+        else:
+            past_len = max(self.past_length)
+            pred_len = max(self.future_length)
+
+        for ts_field in slice_cols:
+            past_piece, future_piece = self._split_array(entry[ts_field], idx, past_length=past_len, future_length=pred_len)
+
+            if self.output_NTC:
+                past_piece = past_piece.transpose()
+                future_piece = future_piece.transpose()
+
+            entry[self._past(ts_field)] = past_piece
+            entry[self._future(ts_field)] = future_piece
+            del entry[ts_field]
+
+        pad_indicator = np.zeros(past_len, dtype=dtype)
+        pad_length = max(past_len - idx, 0)
+        pad_indicator[:pad_length] = 1
+
+        entry[self._past(self.is_pad_field)] = pad_indicator
+        entry[self.forecast_start_field] = (
+            entry[self.start_field] + idx + self.lead_time
+        )
+        entry['context_length'] = past_len
+        entry['prediction_length'] = pred_len
+
+        return entry
+
+    def flatmap_transform(
+            self, entry: DataEntry, is_train: bool
+        ) -> Iterator[DataEntry]:
+        sampled_indices = self.instance_sampler(entry[self.target_field])
+        
+        for idx in sampled_indices:
+            yield self._split_instance(entry, idx, is_train)
\ No newline at end of file
diff --git a/probts/model/forecaster/__init__.py b/probts/model/forecaster/__init__.py
index 98def69..ac33b88 100644
--- a/probts/model/forecaster/__init__.py
+++ b/probts/model/forecaster/__init__.py
@@ -1,3 +1,2 @@
 from .forecaster import Forecaster
-from .point_forecaster import *
-from .prob_forecaster import *
\ No newline at end of file
+from .point_forecaster import *
\ No newline at end of file
diff --git a/probts/model/forecaster/forecaster.py b/probts/model/forecaster/forecaster.py
index 96aa7c2..b3c7caa 100644
--- a/probts/model/forecaster/forecaster.py
+++ b/probts/model/forecaster/forecaster.py
@@ -1,16 +1,15 @@
 import torch
 from torch import nn
 from typing import List
-
 from probts.utils import weighted_average, TemporalScaler
-
+from typing import Union
 
 class Forecaster(nn.Module):
     def __init__(
         self,
         target_dim: int,
-        context_length: int,
-        prediction_length: int,
+        context_length: Union[list,int],
+        prediction_length: Union[list,int],
         freq: str ,
         use_lags: bool = False,
         use_feat_idx_emb: bool = False,
@@ -28,6 +27,17 @@ def __init__(
         
         self.context_length = context_length
         self.prediction_length = prediction_length
+        
+        if isinstance(self.context_length, list):
+            self.max_context_length = max(self.context_length)
+        else:
+            self.max_context_length = self.context_length
+        
+        if isinstance(self.prediction_length, list):
+            self.max_prediction_length = max(self.prediction_length)
+        else:
+            self.max_prediction_length = self.prediction_length
+            
         self.target_dim = target_dim
         self.freq = freq
         self.use_lags = use_lags
@@ -87,13 +97,13 @@ def get_input_sequence(
         past_target_cdf,
         future_target_cdf,
         mode
-    ):
+    ):      
         if mode == 'all':
             sequence = torch.cat((past_target_cdf, future_target_cdf), dim=1)
-            seq_length = self.context_length + self.prediction_length
+            seq_length = self.max_context_length + self.max_prediction_length
         elif mode == 'encode':
             sequence = past_target_cdf
-            seq_length = self.context_length
+            seq_length = self.max_context_length
         elif mode == 'decode':
             sequence = past_target_cdf
             seq_length = 1
@@ -126,9 +136,9 @@ def get_input_time_feat(
     ):
         if mode == 'all':
             time_feat = torch.cat(
-                (past_time_feat[:, -self.context_length:, ...], future_time_feat), dim=1)
+                (past_time_feat[:, -self.max_context_length:, ...], future_time_feat), dim=1)
         elif mode == 'encode':
-            time_feat = past_time_feat[:, -self.context_length:, ...]
+            time_feat = past_time_feat[:, -self.max_context_length:, ...]
         elif mode == 'decode':
             time_feat = future_time_feat
         return time_feat
@@ -154,8 +164,8 @@ def get_inputs(self, batch_data, mode):
     
     def get_scale(self, batch_data):
         self.scaler.fit(
-            batch_data.past_target_cdf[:, -self.context_length:, ...],
-            batch_data.past_observed_values[:, -self.context_length:, ...]
+            batch_data.past_target_cdf[:, -self.max_context_length:, ...],
+            batch_data.past_observed_values[:, -self.max_context_length:, ...]
         )
     
     def get_weighted_loss(self, batch_data, loss):
diff --git a/probts/model/forecaster/point_forecaster/__init__.py b/probts/model/forecaster/point_forecaster/__init__.py
index 63fa456..bf17d83 100644
--- a/probts/model/forecaster/point_forecaster/__init__.py
+++ b/probts/model/forecaster/point_forecaster/__init__.py
@@ -1,4 +1,3 @@
-from .mean import MeanForecaster
 from .linear import LinearForecaster
 from .patchtst import PatchTST
 from .transformer import TransformerForecaster
@@ -6,9 +5,12 @@
 from .dlinear import DLinear
 from .nlinear import NLinear
 from .nhits import NHiTS
-from .timesnet import TimesNet
 from .itransformer import iTransformer
 from .autoformer import Autoformer
+from .transformer_enc import TransformerEnc
+from .tsmixer import TSMixer
+from .elastst import ElasTST
+
 
 # ------- add timesfm to sys.path ----------
 try:
diff --git a/probts/model/forecaster/point_forecaster/autoformer.py b/probts/model/forecaster/point_forecaster/autoformer.py
index 54619e2..14f7887 100644
--- a/probts/model/forecaster/point_forecaster/autoformer.py
+++ b/probts/model/forecaster/point_forecaster/autoformer.py
@@ -12,9 +12,9 @@
 import torch.nn as nn
 from probts.model.forecaster import Forecaster
 from probts.model.nn.layers.Embed import DataEmbedding_wo_pos
-from probts.model.nn.layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
-from probts.model.nn.layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
-
+from probts.model.nn.layers.AutoformerModule.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
+from probts.model.nn.layers.AutoformerModule.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
+import sys
 
 class Autoformer(Forecaster):
     def __init__(
@@ -26,17 +26,17 @@ def __init__(
         e_layers: int = 2,
         d_layers: int = 1,
         output_attention: bool = False,
-        d_ff: int = 512,
-        label_len: int = 48,
+        d_ff: int = 256,
         embed: str = 'timeF',
         dropout: float = 0.1,
-        f_hidden_size: int = 512,
+        f_hidden_size: int = 256,
         **kwargs
     ):
         super().__init__(**kwargs)
-
+        if isinstance(self.context_length, list):
+            self.context_length = max(self.context_length)
         self.label_len = self.context_length
-
+        
         # Decomp
         kernel_size = moving_avg
         self.decomp = series_decomp(kernel_size)
@@ -93,7 +93,7 @@ def __init__(
         )
         self.loss_fn = nn.MSELoss(reduction='none')
         
-    def forward(self, inputs, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, *args, **kwargs):
+    def forward(self, inputs, pred_len, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, *args, **kwargs):
         B, _, _ = inputs.shape
 
         if self.use_time_feat:
@@ -107,8 +107,8 @@ def forward(self, inputs, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=N
             
         
         # decomp init
-        mean = torch.mean(past_target, dim=1).unsqueeze(1).repeat(1, self.prediction_length, 1)
-        zeros = torch.zeros([B, self.prediction_length, self.target_dim], device=past_target.device)
+        mean = torch.mean(past_target, dim=1).unsqueeze(1).repeat(1, pred_len, 1)
+        zeros = torch.zeros([B, pred_len, self.target_dim], device=past_target.device)
         seasonal_init, trend_init = self.decomp(past_target)
         # decoder input
         trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1)
@@ -121,19 +121,22 @@ def forward(self, inputs, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=N
         seasonal_part, trend_part = self.model_decoder(dec_out, enc_out, x_mask=dec_self_mask, cross_mask=dec_enc_mask,
                                                  trend=trend_init)
         # final
+        
         dec_out = trend_part + seasonal_part
-        return dec_out[:, -self.prediction_length:, :]
+        return dec_out[:, -pred_len:, :]
 
     def loss(self, batch_data):
+        max_pred_len = batch_data.max_prediction_length if batch_data.max_prediction_length is not None else max(self.train_prediction_length)
         inputs = self.get_inputs(batch_data, 'all')
-        outputs = self(inputs)
+        outputs = self(inputs, max_pred_len)
         
         loss = self.loss_fn(batch_data.future_target_cdf, outputs)
         loss = self.get_weighted_loss(batch_data, loss)
         return loss.mean()
 
     def forecast(self, batch_data, num_samples=None):
+        max_pred_len = batch_data.future_target_cdf.shape[1]
         inputs = self.get_inputs(batch_data, 'all')
 
-        outputs = self(inputs)
+        outputs = self(inputs, max_pred_len)
         return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/dlinear.py b/probts/model/forecaster/point_forecaster/dlinear.py
index 0da55a3..2397ce4 100644
--- a/probts/model/forecaster/point_forecaster/dlinear.py
+++ b/probts/model/forecaster/point_forecaster/dlinear.py
@@ -11,7 +11,6 @@
 import torch
 import torch.nn as nn
 from probts.model.forecaster import Forecaster
-import sys
 
 class moving_avg(nn.Module):
     """
@@ -61,6 +60,10 @@ def __init__(
         else:
             self.enc_linear = nn.Identity()
 
+        if isinstance(self.prediction_length, list):
+            self.prediction_length = max(self.prediction_length)
+        if isinstance(self.context_length, list):
+            self.context_length = max(self.context_length)
 
         # Decompsition Kernel Size
         self.kernel_size = kernel_size
@@ -77,6 +80,7 @@ def __init__(
         else:
             self.Linear_Seasonal = nn.Linear(self.context_length, self.prediction_length)
             self.Linear_Trend = nn.Linear(self.context_length, self.prediction_length)
+        
         self.loss_fn = nn.MSELoss(reduction='none')
 
     def encoder(self, inputs):
diff --git a/probts/model/forecaster/point_forecaster/elastst.py b/probts/model/forecaster/point_forecaster/elastst.py
new file mode 100644
index 0000000..590c1fb
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/elastst.py
@@ -0,0 +1,194 @@
+import torch
+import torch.nn as nn
+from typing import Union
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.ElasTSTModule.ElasTST_backbone import ElasTST_backbone
+from probts.utils import convert_to_list, weighted_average, InstanceNorm
+
+class ElasTST(Forecaster):
+    def __init__(
+        self,
+        l_patch_size: Union[str, int, list] = '8_16_32',
+        k_patch_size: int = 1,
+        stride: int = None,
+        rotate: bool = True, 
+        addv: bool = False,
+        bin_att: bool = False,
+        rope_theta_init: str = 'exp',
+        min_period: float = 1, 
+        max_period: float = 1000,
+        learn_tem_emb: bool = False,
+        learnable_rope: bool = True, 
+        abs_tem_emb: bool = False,
+        structured_mask: bool = True,
+        max_seq_len: int = 1024,
+        theta_base: float = 10000,
+        t_layers: int = 1, 
+        v_layers: int = 0,
+        patch_share_backbone: bool = True,
+        n_heads: int = 16, 
+        d_k: int = 8, 
+        d_v: int = 8,
+        d_inner: int = 256, 
+        dropout: float = 0.,
+        in_channels: int = 1,
+        f_hidden_size: int = 40,
+        use_norm: bool = True,
+        **kwargs
+    ):
+        """
+        ElasTST model.
+
+        Parameters
+        ----------
+        l_patch_size : Union[str, int, list]
+            Patch sizes configuration.
+        k_patch_size : int
+            Patch size for variables.
+        stride : int
+            Stride for patch splitting. If None, uses patch size as default.
+        rotate : bool
+            Apply rotational positional embeddings.
+        addv : bool
+            Whether to add RoPE information to value in attention. If False, only rotate the key and query embeddings.
+        bin_att : bool
+            Use binary attention biases to encode variate indices (any-variate attention).
+        rope_theta_init : str
+            Initialization for TRoPE, default is 'exp', as used in the paper. Options: ['exp', 'linear', 'uniform', 'rope'].
+        min_period : float
+            Minimum initialized period coefficient for rotary embeddings.
+        max_period : float
+            Maximum initialized period coefficient for rotary embeddings.
+        learn_tem_emb : bool
+            Whether to use learnable temporal embeddings.
+        learnable_rope : bool
+            Make period coefficient in TRoPE learnable.
+        abs_tem_emb : bool
+            Use absolute temporal embeddings if True.
+        structured_mask : bool
+            Apply structured mask or not.
+        max_seq_len : int
+            Maximum sequence length for the input time series.
+        theta_base : int
+            Base frequency of vanilla RoPE.
+        t_layers : int
+            Number of temporal attention layers.
+        v_layers : int
+            Number of variable attention layers.
+        patch_share_backbone : bool
+            Share Transformer backbone across patches.
+        n_heads : int
+            Number of attention heads in the multi-head attention mechanism.
+        d_k : int
+            Dimensionality of key embeddings in attention.
+        d_v : int
+            Dimensionality of value embeddings in attention.
+        d_inner : int
+            Size of inner layers in the feed-forward network.
+        dropout : float
+            Dropout rate for regularization during training.
+        in_channels : int
+            Number of input channels in the time series data. We only consider univariable.
+        f_hidden_size : int
+            Hidden size for the feed-forward layers.
+        use_norm : bool
+            Whether to apply instance normalization.
+        **kwargs : dict
+            Additional keyword arguments for extended functionality.
+        """
+
+        super().__init__(**kwargs)
+        
+        self.l_patch_size = convert_to_list(l_patch_size)
+        self.use_norm = use_norm
+        # Model
+        self.model = ElasTST_backbone(l_patch_size=self.l_patch_size, 
+            stride=stride, 
+            k_patch_size=k_patch_size, 
+            in_channels=in_channels,
+            t_layers=t_layers, 
+            v_layers=v_layers, 
+            hidden_size=f_hidden_size, 
+            d_inner=d_inner,
+            n_heads=n_heads, 
+            d_k=d_k, 
+            d_v=d_v,
+            dropout=dropout,
+            rotate=rotate, 
+            max_seq_len=max_seq_len, 
+            theta=theta_base,
+            addv=addv, 
+            bin_att=bin_att,
+            learn_tem_emb=learn_tem_emb, 
+            abs_tem_emb=abs_tem_emb, 
+            learnable_theta=learnable_rope, 
+            structured_mask=structured_mask,
+            rope_theta_init=rope_theta_init, 
+            min_period=min_period, 
+            max_period=max_period,
+            patch_share_backbone=patch_share_backbone
+        )
+        
+        self.loss_fn = nn.MSELoss(reduction='none')
+        self.instance_norm = InstanceNorm()
+    
+    def forward(self, batch_data, pred_len, dataset_name=None):
+        new_pred_len = pred_len
+        for p in self.l_patch_size:
+            new_pred_len = self.check_divisibility(new_pred_len, p)
+        
+        B, _, K = batch_data.past_target_cdf.shape
+        past_target = batch_data.past_target_cdf
+        past_observed_values = batch_data.past_observed_values
+        
+        if self.use_norm:
+            past_target = self.instance_norm(past_target, 'norm')
+
+        # future_observed_values is the mask indicate whether there is a value in a position
+        future_observed_values = torch.zeros([B, new_pred_len, K]).to(batch_data.future_observed_values.device)
+
+        pred_len = batch_data.future_observed_values.shape[1]
+        future_observed_values[:,:pred_len] = batch_data.future_observed_values
+
+        # target placeholder
+        future_placeholder = torch.zeros([B, new_pred_len, K]).to(batch_data.past_target_cdf.device)
+
+        x, pred_list = self.model(past_target, future_placeholder, past_observed_values, future_observed_values, dataset_name=dataset_name)
+        dec_out = x[:, :pred_len]
+        if self.use_norm:
+            dec_out = self.instance_norm(dec_out, 'denorm')
+
+        return dec_out # [b l k], [b l k #patch_size]
+
+
+    def loss(self, batch_data, reduce='none'):
+        max_pred_len = batch_data.max_prediction_length if batch_data.max_prediction_length is not None else max(self.train_prediction_length)
+            
+        predict = self(batch_data, max_pred_len, dataset_name=None, )
+        target = batch_data.future_target_cdf
+        
+        observed_values = batch_data.future_observed_values
+        loss = self.loss_fn(target, predict)
+
+        loss = self.get_weighted_loss(observed_values, loss, reduce=reduce)
+        
+        if reduce=='mean':
+            loss = loss.mean()
+        return loss
+
+    def forecast(self, batch_data, num_samples=None):
+        # max_pred_len = batch_data.max_prediction_length if batch_data.max_prediction_length is not None else max(self.prediction_length)
+        max_pred_len = batch_data.future_target_cdf.shape[1]
+        outputs = self(batch_data, max_pred_len, dataset_name=None, )
+        return outputs.unsqueeze(1)
+    
+    def check_divisibility(self, pred_len, patch_size):
+        if pred_len % patch_size == 0:
+            return pred_len
+        else:  
+            return (pred_len // patch_size + 1) * patch_size  
+
+    def get_weighted_loss(self, observed_values, loss, reduce='mean'):
+        loss_weights, _ = observed_values.min(dim=-1, keepdim=True)
+        loss = weighted_average(loss, weights=loss_weights, dim=1, reduce=reduce)
+        return loss
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/forecastpfn.py b/probts/model/forecaster/point_forecaster/forecastpfn.py
deleted file mode 100644
index 0223205..0000000
--- a/probts/model/forecaster/point_forecaster/forecastpfn.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from ForecastPFN
-# - Source: https://github.com/abacusai/ForecastPFN
-# - Paper: ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
-# - License: Apache License 2.0
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import datetime
-
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-import torch
-from keras import backend
-from sklearn.preprocessing import StandardScaler
-
-from probts.model.forecaster import Forecaster
-
-
-def smape(y_true, y_pred):
-    """ Calculate Armstrong's original definition of sMAPE between `y_true` & `y_pred`.
-        `loss = 200 * mean(abs((y_true - y_pred) / (y_true + y_pred), axis=-1)`
-        Args:
-        y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
-        y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
-        Returns:
-        Symmetric mean absolute percentage error values. shape = `[batch_size, d0, ..
-        dN-1]`.
-        """
-    y_pred = tf.convert_to_tensor(y_pred)
-    y_true = tf.cast(y_true, y_pred.dtype)
-    diff = tf.abs(
-        (y_true - y_pred) /
-        backend.maximum(y_true + y_pred, backend.epsilon())
-    )
-    return 200.0 * backend.mean(diff, axis=-1)
-
-
-class ForecastPFN(Forecaster):
-    def __init__(
-        self,
-        label_len: int = 48,
-        ckpt_path: str = None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.no_training = True
-
-        self.label_len = label_len
-        
-        self.model = tf.keras.models.load_model(ckpt_path, custom_objects={'smape': smape})
-
-
-    def _ForecastPFN_time_features(self, x_mark_enc: np.ndarray, x_mark_dec: np.ndarray):
-        def extract_time_features(ts):
-            original_shape = ts.shape
-            ts = ts.reshape(-1)  # Flatten the array
-            if type(ts[0]) == datetime.datetime:
-                year = np.array([x.year for x in ts])
-                month = np.array([x.month for x in ts])
-                day = np.array([x.day for x in ts])
-                day_of_week = np.array([x.weekday() + 1 for x in ts])
-                day_of_year = np.array([x.timetuple().tm_yday for x in ts])
-            else:
-                ts = pd.to_datetime(ts)
-                year = ts.year.values
-                month = ts.month.values
-                day = ts.day.values
-                day_of_week = ts.day_of_week.values + 1
-                day_of_year = ts.day_of_year.values
-            
-            features = np.stack([year, month, day, day_of_week, day_of_year], axis=-1)
-            return features.reshape(*original_shape, -1).squeeze()
-
-        # Process the encoder and decoder inputs
-        x_mark_enc_features = extract_time_features(x_mark_enc)
-        x_mark_dec_features = extract_time_features(x_mark_dec)
-
-        return x_mark_enc_features, x_mark_dec_features
-
-    def _process_tuple(self, x, x_mark, y_mark, horizon):
-        """
-        x: tensor of shape (n, 1)
-        x_mark: tensor of shape (n, d)
-        y_mark: tensor of shape (horizon, d)
-
-        where
-        n       is the input sequence length
-        horizon is the output sequence length
-        d is the dimensionality of the time_stamp (5 for ForecastPFN)
-        """
-        if tf.reduce_all(x == x[0]):
-            x = tf.concat([x[:-1], x[-1:] + 1], axis=0)
-        
-        history = x.numpy()
-        scaler = StandardScaler()
-        scaler.fit(history)
-        history = scaler.transform(history)
-        
-        history_mean = np.nanmean(history[-6:])
-        history_std = np.nanstd(history[-6:])
-        local_scale = history_mean + history_std + 1e-4
-        
-        history = np.clip(history / local_scale, a_min=0, a_max=1)
-        
-        if x.shape[0] != 100:
-            if x.shape[0] > 100:
-                target = x_mark[-100:, :]
-                history = history[-100:, :]
-            else:
-                target = tf.pad(x_mark, [[100 - x.shape[0], 0], [0, 0]])
-                history = tf.pad(history, [[100 - x.shape[0], 0], [0, 0]])
-            
-            history = tf.repeat(tf.expand_dims(history, axis=0), horizon, axis=0)[:, :, 0]
-            ts = tf.repeat(tf.expand_dims(target, axis=0), horizon, axis=0)
-        else:
-            ts = tf.repeat(tf.expand_dims(x_mark, axis=0), horizon, axis=0)
-            history = tf.convert_to_tensor(history, dtype=tf.float32)
-        
-        task = tf.fill([horizon], 1)
-        y_mark_tensor = tf.convert_to_tensor(y_mark[-horizon:, :], dtype=tf.int64)
-        target_ts = tf.expand_dims(y_mark_tensor, axis=1)
-        
-        model_input = {'ts': ts, 'history': history, 'target_ts': target_ts, 'task': task}
-        pred_vals = self.model(model_input)
-        
-        scaled_vals = pred_vals['result'].numpy().T.reshape(-1) * pred_vals['scale'].numpy().reshape(-1)
-        scaled_vals = scaler.inverse_transform([scaled_vals])
-        return scaled_vals
-    
-    
-    def _process_batch(self, batch_x, batch_y, batch_x_mark, batch_y_mark):
-        preds = []
-        for idx, (x, y, x_mark, y_mark) in enumerate(zip(batch_x, batch_y, batch_x_mark, batch_y_mark)):
-            pred = self._process_tuple(x, x_mark, y_mark, self.prediction_length)
-            preds.append(pred)
-        return preds
-
-
-    def forecast(self, batch_data, num_samples=None):
-        # For now, we only support batch_size=1
-        B, _, K = batch_data.past_target_cdf.shape
-        inputs = batch_data.past_target_cdf[:, -self.context_length:, ...].cpu()
-        x_mark_enc = batch_data.past_time_feat[:, -self.context_length:, ...].cpu().numpy().astype('datetime64[s]')
-        x_mark_dec = batch_data.future_time_feat.cpu().numpy().astype('datetime64[s]')
-        x_mark_enc, x_mark_dec = self._ForecastPFN_time_features(x_mark_enc, x_mark_dec)
-
-        x_mark_dec = tf.concat([x_mark_enc[:, -self.label_len:, :], x_mark_dec], axis=1)
-        
-        inputs = tf.reshape(inputs, [-1, self.context_length, 1])
-        x_mark_enc = tf.repeat(x_mark_enc, repeats=K, axis=0)
-        x_mark_dec = tf.repeat(x_mark_dec, repeats=K, axis=0)
-        
-        dec_inp = tf.zeros_like(inputs[:, -self.prediction_length:, :])
-        dec_inp = tf.concat([inputs[:, -self.label_len:, :], dec_inp], axis=1)
-        x_mark_enc = tf.cast(x_mark_enc, tf.int64)
-        x_mark_dec = tf.cast(x_mark_dec, tf.int64)
-        
-        outputs = self._process_batch(inputs, dec_inp, x_mark_enc, x_mark_dec)
-        outputs = tf.concat(outputs, axis=0)
-        outputs = tf.reshape(outputs, [B, -1, K])
-        outputs = outputs[:, :self.prediction_length, :].numpy()
-        outputs = torch.tensor(outputs)
-        return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/gru.py b/probts/model/forecaster/point_forecaster/gru.py
index 47feeac..b0954f8 100644
--- a/probts/model/forecaster/point_forecaster/gru.py
+++ b/probts/model/forecaster/point_forecaster/gru.py
@@ -17,6 +17,12 @@ def __init__(
         super().__init__(**kwargs)
         self.autoregressive = True
         
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+        
         self.model = nn.GRU(
             input_size=self.input_size,
             hidden_size=f_hidden_size,
diff --git a/probts/model/forecaster/point_forecaster/itransformer.py b/probts/model/forecaster/point_forecaster/itransformer.py
index 07ca249..a7b9d81 100644
--- a/probts/model/forecaster/point_forecaster/itransformer.py
+++ b/probts/model/forecaster/point_forecaster/itransformer.py
@@ -35,6 +35,12 @@ def __init__(
         
         self.label_len = label_len
         
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+            
         self.use_norm = use_norm
         # Embedding
         self.enc_embedding = DataEmbedding_inverted(self.context_length, f_hidden_size,
diff --git a/probts/model/forecaster/point_forecaster/linear.py b/probts/model/forecaster/point_forecaster/linear.py
index 9f3243b..e7ab4b8 100644
--- a/probts/model/forecaster/point_forecaster/linear.py
+++ b/probts/model/forecaster/point_forecaster/linear.py
@@ -23,6 +23,12 @@ def __init__(
         super().__init__(**kwargs)
         self.individual = individual
         
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+        
         if self.individual:
             self.linear = nn.ModuleList()
             for i in range(self.input_size):
diff --git a/probts/model/forecaster/point_forecaster/mean.py b/probts/model/forecaster/point_forecaster/mean.py
deleted file mode 100644
index 2529ed8..0000000
--- a/probts/model/forecaster/point_forecaster/mean.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-from einops import repeat
-from probts.model.forecaster import Forecaster
-
-
-class MeanForecaster(Forecaster):
-    def __init__(
-        self,
-        global_mean: torch.Tensor,
-        mode: str = 'batch',
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.global_mean = global_mean
-        self.mode = mode
-        self.no_training = True
-
-    @property
-    def name(self):
-        return self.mode + self.__class__.__name__
-        
-    def forecast(self, batch_data, num_samples=None):
-        B = batch_data.past_target_cdf.shape[0]
-        if self.mode == 'global':
-            outputs = self.global_mean.clone()
-        elif self.mode == 'batch':
-            outputs = torch.mean(batch_data.past_target_cdf, dim=1)
-            outputs = torch.mean(outputs, dim=0)
-        else:
-            raise ValueError(f"Unsupported mode: {self.mode}")
-            
-        outputs = repeat(outputs,'d -> b n l d', b=B, n=1, l=self.prediction_length)
-        return outputs
diff --git a/probts/model/forecaster/point_forecaster/nhits.py b/probts/model/forecaster/point_forecaster/nhits.py
index dcdbca1..9974335 100644
--- a/probts/model/forecaster/point_forecaster/nhits.py
+++ b/probts/model/forecaster/point_forecaster/nhits.py
@@ -286,6 +286,12 @@ def __init__(
 
         n_layers = [n_layers] * n_stacks
         hidden_size = n_stacks * [2 * [hidden_size]]
+        
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
 
         if pooling_sizes is None:
             pooling_sizes = np.exp2(np.round(np.linspace(0.49, np.log2(self.prediction_length / 2), n_stacks)))
diff --git a/probts/model/forecaster/point_forecaster/nlinear.py b/probts/model/forecaster/point_forecaster/nlinear.py
index 254951a..bb68e8b 100644
--- a/probts/model/forecaster/point_forecaster/nlinear.py
+++ b/probts/model/forecaster/point_forecaster/nlinear.py
@@ -26,6 +26,12 @@ def __init__(
             )
         else:
             self.enc_linear = nn.Identity()
+            
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
 
         self.target_dim = self.target_dim
         self.individual = individual
diff --git a/probts/model/forecaster/point_forecaster/patchtst.py b/probts/model/forecaster/point_forecaster/patchtst.py
index 90020c3..70a2c95 100644
--- a/probts/model/forecaster/point_forecaster/patchtst.py
+++ b/probts/model/forecaster/point_forecaster/patchtst.py
@@ -5,7 +5,6 @@
 # - License: Apache-2.0
 
 # We thank the authors for their contributions.
-# -----
 # ----------------------------------------------------------------------------
 
 
@@ -14,8 +13,8 @@
 from typing import Optional
 
 from probts.model.forecaster import Forecaster
-from probts.model.nn.layers.PatchTST_backbone import PatchTST_backbone
-from probts.model.nn.layers.PatchTST_layers import series_decomp
+from probts.model.nn.layers.PatchTSTModule.PatchTST_backbone import PatchTST_backbone
+from probts.model.nn.layers.PatchTSTModule.PatchTST_layers import series_decomp
 
 class PatchTST(Forecaster):
     def __init__(
@@ -61,6 +60,11 @@ def __init__(
         else:
             self.enc_linear = nn.Identity()
 
+        if isinstance(self.prediction_length, list):
+            self.prediction_length = self.prediction_length[0]
+        if isinstance(self.context_length, list):
+            self.context_length = self.context_length[0]
+        
         # Load parameters
         c_in = self.input_size
         context_window = self.context_length
@@ -77,7 +81,7 @@ def __init__(
                                   attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                   pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                   pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
-                                  subtract_last=subtract_last)
+                                  subtract_last=subtract_last,)
             self.model_res = PatchTST_backbone(c_in=c_in, context_window=context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
                                   max_seq_len=max_seq_len, n_layers=n_layers, d_model=f_hidden_size,
                                   n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, attn_dropout=attn_dropout,
@@ -85,7 +89,7 @@ def __init__(
                                   attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                   pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                   pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
-                                  subtract_last=subtract_last)
+                                  subtract_last=subtract_last,)
         else:
             self.model = PatchTST_backbone(c_in=c_in, context_window=context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
                                   max_seq_len=max_seq_len, n_layers=n_layers, d_model=f_hidden_size,
@@ -94,7 +98,7 @@ def __init__(
                                   attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                   pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                   pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
-                                  subtract_last=subtract_last)
+                                  subtract_last=subtract_last,)
         self.loss_fn = nn.MSELoss(reduction='none')
     
     def forward(self, x):
diff --git a/probts/model/forecaster/point_forecaster/timer.py b/probts/model/forecaster/point_forecaster/timer.py
deleted file mode 100644
index b4a2ba3..0000000
--- a/probts/model/forecaster/point_forecaster/timer.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from Large-Time-Series-Model
-# - Source: https://github.com/thuml/Large-Time-Series-Model
-# - Paper: Timer: Generative Pre-trained Transformers Are Large Time Series Models
-# - License: MIT License
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-from einops import rearrange, repeat
-from torch import nn
-
-from probts.model.forecaster import Forecaster
-
-
-class Model(nn.Module):
-    """
-    Paper link: https://arxiv.org/pdf/2402.02368.pdf
-    """
-
-    def __init__(self, ckpt_path):
-        super().__init__()
-        if ckpt_path and ckpt_path != "":
-            if ckpt_path.endswith('.pt'):
-                # print(f"Loading Timer model from {ckpt_path}")
-                self.timer = torch.jit.load(ckpt_path)
-        else:
-            raise NotImplementedError
-
-    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
-        return self.timer(x_enc, x_mark_enc, x_dec, x_mark_dec)
-
-
-class Timer(Forecaster):
-    def __init__(
-        self,
-        label_len: int = 576,
-        ckpt_path: str = None,
-        ckpt_path_finetune: str = None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.no_training = True
-        
-        self.output_patch_len = 96 # fixed by the pre-trained model
-        self.label_len = label_len
-
-        # Load Timer
-        self.model = Model(ckpt_path)
-        if ckpt_path_finetune:
-            print(f"Loading Timer finetune model from {ckpt_path_finetune}")
-            self.model.load_state_dict(torch.load(ckpt_path_finetune))
-       
-
-    def forecast(self, batch_data, num_samples=None):        
-        # for now, we only support batch_size=1
-        B, _, K = batch_data.past_target_cdf.shape
-        inputs = batch_data.past_target_cdf[:, -self.context_length:, ...]
-        x_mark_enc = batch_data.past_time_feat[:, -self.context_length:, ...]
-        x_mark_dec = batch_data.future_time_feat
-        x_mark_dec = torch.cat([x_mark_enc[:, -self.label_len:, :], x_mark_dec], dim=1)
-
-        inputs = rearrange(inputs, 'b l k -> (b k) l 1')
-        x_mark_enc = repeat(x_mark_enc, 'b l f -> (b k) l f', k=K)
-        x_mark_dec = repeat(x_mark_dec, 'b l f -> (b k) l f', k=K)
-
-        dec_inp = torch.zeros_like(inputs[:, -self.prediction_length:, :]).float()
-        dec_inp = torch.cat((inputs[:, -self.label_len:, ...], dec_inp), dim=1).float()
-
-        inference_steps = self.prediction_length // self.output_patch_len
-        dis = self.prediction_length - inference_steps * self.output_patch_len
-        if dis != 0:
-            inference_steps += 1
-
-        pred_y = []
-
-        for j in range(inference_steps):
-            if len(pred_y) != 0:
-                inputs = torch.cat([inputs[:, self.output_patch_len:, :], pred_y[-1]], dim=1)
-                tmp = x_mark_dec[:, j - 1:j, :]
-                x_mark_enc = torch.cat([x_mark_enc[:, 1:, :], tmp], dim=1)
-
-            outputs = self.model(inputs, x_mark_enc, dec_inp, x_mark_dec)
-            pred_y.append(outputs[:, -self.output_patch_len:, :])
-
-        pred_y = torch.cat(pred_y, dim=1)
-        if dis != 0:
-            pred_y = pred_y[:, :-dis, :]
-        pred_y = rearrange(pred_y, '(b k) l 1 -> b l k', b=B, k=K)
-        pred_y = pred_y[:, :self.prediction_length, :]
-        return pred_y.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/timesfm.py b/probts/model/forecaster/point_forecaster/timesfm.py
index b24ec22..48fb276 100644
--- a/probts/model/forecaster/point_forecaster/timesfm.py
+++ b/probts/model/forecaster/point_forecaster/timesfm.py
@@ -28,6 +28,12 @@ def __init__(
         super().__init__(**kwargs)
         self.no_training = True
         
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+        
         if (type(self.target_dim).__name__=='dict'):
             for dataset_name in self.target_dim:
                 target_dim = target_dim[dataset_name]
@@ -35,13 +41,6 @@ def __init__(
         else:
             freq = self.freq
                 
-        if (type(self.context_length).__name__=='list'):
-            context_length = context_length[0]
-            
-        if (type(self.prediction_length).__name__=='list'):
-            # prediction_length = prediction_length[0]
-            prediction_length = max(prediction_length)
-        
         self.tfm = TimesFm(
             context_len=self.context_length,
             horizon_len=self.prediction_length,
@@ -57,21 +56,20 @@ def __init__(
         freq = freq.lower()
         
         if freq in freq_dict:
-            self.freq_int = freq_dict[freq]
+            self.local_freq = freq_dict[freq]
         else:
-            self.freq_int = 0
+            self.local_freq = 0
 
-        print(f"TimesFM - frequency: {freq}, freq_num: {self.freq_int}")
+        print(f"TimesFM - frequency: {freq}, freq_num: {self.local_freq}")
     
 
     def forecast(self, batch_data, num_samples=None):
         inputs = self.get_inputs(batch_data, 'encode')
         inputs = inputs[:, -self.context_length:].cpu()
         B, _, K = inputs.shape
-        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
-        
+
         inputs = np.array(rearrange(inputs, 'b l k -> (b k) l'))
-        frequency_input = [self.freq_int] * inputs.shape[0]
+        frequency_input = [self.local_freq] * inputs.shape[0]
         
         _, out = self.tfm.forecast(
             inputs,
diff --git a/probts/model/forecaster/point_forecaster/timesnet.py b/probts/model/forecaster/point_forecaster/timesnet.py
deleted file mode 100644
index f64ff21..0000000
--- a/probts/model/forecaster/point_forecaster/timesnet.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from TSLib
-# - Source: https://github.com/libts/tslib
-# - Paper: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
-# - License:  LGPL-2.1
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.fft
-from probts.model.forecaster import Forecaster
-from probts.model.nn.layers.Embed import DataEmbedding
-from probts.model.nn.layers.Conv_Blocks import Inception_Block_V1
-
-
-def FFT_for_Period(x, k=2):
-    # [B, T, C]
-    xf = torch.fft.rfft(x, dim=1)
-    # find period by amplitudes
-    frequency_list = abs(xf).mean(0).mean(-1)
-    frequency_list[0] = 0
-    _, top_list = torch.topk(frequency_list, k)
-    top_list = top_list.detach().cpu().numpy()
-    period = x.shape[1] // top_list
-    return period, abs(xf).mean(-1)[:, top_list]
-
-
-class TimesBlock(nn.Module):
-    def __init__(self, context_length, prediction_length, top_k, d_model, d_ff, num_kernels):
-        super(TimesBlock, self).__init__()
-        self.seq_len = context_length
-        self.pred_len = prediction_length
-        self.k = top_k
-        # parameter-efficient design
-        self.conv = nn.Sequential(
-            Inception_Block_V1(d_model, d_ff,
-                               num_kernels=num_kernels),
-            nn.GELU(),
-            Inception_Block_V1(d_ff, d_model,
-                               num_kernels=num_kernels)
-        )
-
-    def forward(self, x):
-        B, T, N = x.size()
-        period_list, period_weight = FFT_for_Period(x, self.k)
-
-        res = []
-        for i in range(self.k):
-            period = period_list[i]
-            # padding
-            if (self.seq_len + self.pred_len) % period != 0:
-                length = (
-                                 ((self.seq_len + self.pred_len) // period) + 1) * period
-                padding = torch.zeros([x.shape[0], (length - (self.seq_len + self.pred_len)), x.shape[2]]).to(x.device)
-                out = torch.cat([x, padding], dim=1)
-            else:
-                length = (self.seq_len + self.pred_len)
-                out = x
-            # reshape
-            out = out.reshape(B, length // period, period,
-                              N).permute(0, 3, 1, 2).contiguous()
-            # 2D conv: from 1d Variation to 2d Variation
-            out = self.conv(out)
-            # reshape back
-            out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
-            res.append(out[:, :(self.seq_len + self.pred_len), :])
-        res = torch.stack(res, dim=-1)
-        # adaptive aggregation
-        period_weight = F.softmax(period_weight, dim=1)
-        period_weight = period_weight.unsqueeze(
-            1).unsqueeze(1).repeat(1, T, N, 1)
-        res = torch.sum(res * period_weight, -1)
-        # residual connection
-        res = res + x
-        return res
-
-
-class TimesNet(Forecaster):
-    def __init__(
-        self,
-        n_layers: int = 2,
-        num_kernels: int = 6,
-        top_k: int = 5,
-        d_ff: int = 32,
-        embed: str = 'timeF',
-        dropout: float = 0.1,
-        f_hidden_size: int = 40,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.seq_len = self.context_length
-        self.pred_len = self.prediction_length
-
-        self.model = nn.ModuleList(
-            [TimesBlock(self.context_length, self.prediction_length, top_k, f_hidden_size, d_ff, num_kernels)
-                for _ in range(n_layers)]
-        )
-        self.enc_embedding = DataEmbedding(self.target_dim, f_hidden_size, embed, self.freq.lower(), dropout)
-        self.layer = n_layers
-        self.layer_norm = nn.LayerNorm(f_hidden_size)
-
-        self.predict_linear = nn.Linear(
-            self.seq_len, self.pred_len + self.seq_len)
-        self.projection = nn.Linear(
-            f_hidden_size, self.target_dim, bias=True)
-        
-        if self.input_size != self.target_dim:
-            self.enc_linear = nn.Linear(
-                in_features=self.input_size, out_features=self.target_dim
-            )
-        else:
-            self.enc_linear = nn.Identity()
-        self.loss_fn = nn.MSELoss(reduction='none')
-
-    def forward(self, x_enc, x_mark_enc=None):
-        # Normalization from Non-stationary Transformer
-        means = x_enc.mean(1, keepdim=True).detach()
-        x_enc = x_enc - means
-        stdev = torch.sqrt(
-            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
-        x_enc = x_enc / stdev
-
-        # embedding
-        enc_out = self.enc_embedding(x_enc, x_mark_enc)  # [B,T,C]
-        enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(
-            0, 2, 1)  # align temporal dimension
-        # TimesNet
-        for i in range(self.layer):
-            enc_out = self.layer_norm(self.model[i](enc_out))
-        # porject back
-        dec_out = self.projection(enc_out)
-
-        # De-Normalization from Non-stationary Transformer
-        dec_out = dec_out * \
-                  (stdev[:, 0, :].unsqueeze(1).repeat(
-                      1, self.pred_len + self.seq_len, 1))
-        dec_out = dec_out + \
-                  (means[:, 0, :].unsqueeze(1).repeat(
-                      1, self.pred_len + self.seq_len, 1))
-        return dec_out[:, -self.pred_len:, :]  # [B, L, D]
-
-
-    def loss(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'all')
-        inputs = inputs[:, : self.context_length, ...]
-        inputs = self.enc_linear(inputs)
-        # x: [Batch, Input length, Channel]
-        outputs = self(inputs)
-    
-        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples=None):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = self.enc_linear(inputs)
-        outputs = self(inputs)
-        return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/tinytimemixer.py b/probts/model/forecaster/point_forecaster/tinytimemixer.py
deleted file mode 100644
index a3dc47e..0000000
--- a/probts/model/forecaster/point_forecaster/tinytimemixer.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from granite-tsfm
-# - Source: https://github.com/ibm-granite/granite-tsfm
-# - Paper: Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
-# - License: Apache License 2.0
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-from probts.model.forecaster import Forecaster
-
-from submodules.tsfm.tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction
-
-
-class TinyTimeMixer(Forecaster):
-    """
-    TinyTimeMixer from https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb
-    prediction length originally 96
-    context length originally 512
-    changes might cause degradation in performance
-    """
-
-    def __init__(
-        self,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.no_training = True
-
-        # TTM model branch
-        # Use main for 512-96 model
-        # Use "1024_96_v1" for 1024-96 model
-        TTM_MODEL_REVISION = "main"
-
-        self.zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(
-            "ibm/TTM", revision=TTM_MODEL_REVISION
-        )
-        
-
-    def forecast(self, batch_data, num_samples=None):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = inputs[:, -self.context_length:]
-        B, _, K = inputs.shape 
-        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
-        self.zeroshot_model.eval()
-        point_forecast = self.zeroshot_model.forward(inputs).prediction_outputs
-        return point_forecast.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/transformer.py b/probts/model/forecaster/point_forecaster/transformer.py
index ac09f00..17e86e0 100644
--- a/probts/model/forecaster/point_forecaster/transformer.py
+++ b/probts/model/forecaster/point_forecaster/transformer.py
@@ -21,6 +21,12 @@ def __init__(
         super().__init__(**kwargs)
         self.autoregressive = True
         self.f_hidden_size = f_hidden_size
+        
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
 
         self.enc_linear = nn.Linear(self.input_size, self.f_hidden_size)
         self.dec_linear = nn.Linear(self.input_size, self.f_hidden_size)
@@ -41,7 +47,7 @@ def __init__(
         self.linear = nn.Linear(self.f_hidden_size, self.target_dim)
         self.loss_fn = nn.MSELoss(reduction='none')
 
-    def loss(self, batch_data):
+    def loss(self, batch_data, training_range=None):
         inputs = self.get_inputs(batch_data, 'all') # [B L D]
 
         # Encode
diff --git a/probts/model/forecaster/point_forecaster/transformer_enc.py b/probts/model/forecaster/point_forecaster/transformer_enc.py
new file mode 100644
index 0000000..b2154c6
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/transformer_enc.py
@@ -0,0 +1,107 @@
+import sys
+import torch
+import torch.nn as nn
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.Transformer_EncDec import Encoder, EncoderLayer
+from probts.model.nn.layers.SelfAttention_Family import FullAttention, AttentionLayer
+
+class TransformerEnc(Forecaster):
+    def __init__(
+        self,
+        factor: int = 1,
+        n_heads: int = 8,
+        activation: str = 'gelu',
+        e_layers: int = 2,
+        output_attention: bool = False,
+        d_ff: int = 512,
+        label_len: int = 48,
+        use_norm: bool = True,
+        dropout: float = 0.1,
+        f_hidden_size: int = 512,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        self.label_len = label_len
+        self.use_norm = use_norm
+        
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+            
+        self.enc_embedding = nn.Linear(self.target_dim, f_hidden_size)
+
+        # Encoder-only architecture
+        self.model_encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, factor, attention_dropout=dropout,
+                                      output_attention=output_attention), f_hidden_size, n_heads),
+                    f_hidden_size,
+                    d_ff,
+                    dropout=dropout,
+                    activation=activation
+                ) for l in range(e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(f_hidden_size)
+        )
+
+        self.len_projector = nn.Linear(self.context_length, self.prediction_length)
+        self.dim_projector = nn.Linear(f_hidden_size, self.target_dim)
+        self.loss_fn = nn.MSELoss(reduction='none')
+
+    def forward(self, inputs):
+        if self.use_time_feat:
+            past_target = inputs[:,:,:self.target_dim]
+            x_mark_enc = inputs[:,:,-self.target_dim:]
+        else:
+            past_target = inputs
+            x_mark_enc = None
+            
+        
+        if self.use_norm:
+            # Normalization from Non-stationary Transformer
+            means = past_target.mean(1, keepdim=True).detach()
+            past_target = past_target - means
+            stdev = torch.sqrt(torch.var(past_target, dim=1, keepdim=True, unbiased=False) + 1e-5)
+            past_target /= stdev
+
+        _, _, N = past_target.shape # B L N
+        # B: batch_size;    E: d_model; 
+        # L: seq_len;       S: pred_len;
+        # N: number of variate (tokens), can also includes covariates
+
+        # Embedding
+        # B L N -> B N E                (B L N -> B L E in the vanilla Transformer)
+        enc_out = self.enc_embedding(past_target) # covariates (e.g timestamp) can be also embedded as tokens
+        
+        # B N E -> B N E                (B L E -> B L E in the vanilla Transformer)
+        enc_out, attns = self.model_encoder(enc_out, attn_mask=None)
+
+        # B N E -> B N S -> B S N 
+        dec_out = self.len_projector(enc_out.permute(0, 2, 1)) 
+        dec_out = self.dim_projector(dec_out.permute(0, 2, 1)) # filter the covariates
+
+        if self.use_norm:
+            # De-Normalization from Non-stationary Transformer
+            dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.prediction_length, 1))
+            dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.prediction_length, 1))
+
+        return dec_out[:, -self.prediction_length:, :]
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        output = self(inputs)
+
+        return output.unsqueeze(1)
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/tsmixer.py b/probts/model/forecaster/point_forecaster/tsmixer.py
new file mode 100644
index 0000000..b5ddb40
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/tsmixer.py
@@ -0,0 +1,151 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from TSMixer
+# - Source: https://github.com/google-research/google-research/tree/master/tsmixer
+# - Paper: TSMixer: An All-MLP Architecture for Time Series Forecasting
+# - License: Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from probts.model.nn.layers.TSMixer_layers import MixerLayer, TimeBatchNorm2d, feature_to_time, time_to_feature
+
+
+from probts.model.forecaster import Forecaster
+import sys
+
+
+class TSMixer(Forecaster):
+    """TSMixer model for time series forecasting.
+
+    This model uses a series of mixer layers to process time series data,
+    followed by a linear transformation to project the output to the desired
+    prediction length.
+
+    Attributes:
+        mixer_layers: Sequential container of mixer layers.
+        temporal_projection: Linear layer for temporal projection.
+
+    Args:
+        sequence_length: Length of the input time series sequence.
+        prediction_length: Desired length of the output prediction sequence.
+        input_channels: Number of input channels.
+        output_channels: Number of output channels. Defaults to None.
+        activation_fn: Activation function to use. Defaults to "relu".
+        num_blocks: Number of mixer blocks. Defaults to 2.
+        dropout_rate: Dropout rate for regularization. Defaults to 0.1.
+        ff_dim: Dimension of feedforward network inside mixer layer. Defaults to 64.
+        normalize_before: Whether to apply layer normalization before or after mixer layer.
+        norm_type: Type of normalization to use. "batch" or "layer". Defaults to "batch".
+    """
+
+    def __init__(
+        self,
+        activation_fn: str = "relu",
+        num_blocks: int = 2,
+        dropout_rate: float = 0.1,
+        ff_dim: int = 64,
+        normalize_before: bool = True,
+        norm_type: str = "batch",
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        # Transform activation_fn to callable
+        activation_fn = getattr(F, activation_fn)
+        
+        input_channels = self.target_dim
+        output_channels = self.target_dim
+        
+        if type(self.prediction_length) == list:
+            self.prediction_length = max(self.prediction_length)
+
+        if type(self.context_length) == list:
+            self.context_length = max(self.context_length)
+            
+        sequence_length = self.context_length
+        prediction_length = self.prediction_length
+        # Transform norm_type to callable
+        assert norm_type in {
+            "batch",
+            "layer",
+        }, f"Invalid norm_type: {norm_type}, must be one of batch, layer."
+        norm_type = TimeBatchNorm2d if norm_type == "batch" else nn.LayerNorm
+
+        # Build mixer layers
+        self.mixer_layers = self._build_mixer(
+            num_blocks,
+            input_channels,
+            output_channels,
+            ff_dim=ff_dim,
+            activation_fn=activation_fn,
+            dropout_rate=dropout_rate,
+            sequence_length=sequence_length,
+            normalize_before=normalize_before,
+            norm_type=norm_type,
+        )
+
+        # Temporal projection layer
+        self.temporal_projection = nn.Linear(sequence_length, prediction_length)
+        self.loss_fn = nn.MSELoss(reduction='none')
+
+    def _build_mixer(
+        self, num_blocks: int, input_channels: int, output_channels: int, **kwargs
+    ):
+        """Build the mixer blocks for the model.
+
+        Args:
+            num_blocks (int): Number of mixer blocks to be built.
+            input_channels (int): Number of input channels for the first block.
+            output_channels (int): Number of output channels for the last block.
+            **kwargs: Additional keyword arguments for mixer layer configuration.
+
+        Returns:
+            nn.Sequential: Sequential container of mixer layers.
+        """
+        output_channels = output_channels if output_channels is not None else input_channels
+        channels = [input_channels] * (num_blocks - 1) + [output_channels]
+    
+
+        return nn.Sequential(
+            *[
+                MixerLayer(input_channels=in_ch, output_channels=out_ch, **kwargs)
+                for in_ch, out_ch in zip(channels[:-1], channels[1:])
+            ]
+        )
+
+    def forward(self, x_hist: torch.Tensor) -> torch.Tensor:
+        """Forward pass of the TSMixer model.
+
+        Args:
+            x_hist (torch.Tensor): Input time series tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after processing by the model.
+        """
+        x = self.mixer_layers(x_hist)
+
+        x_temp = feature_to_time(x)
+        x_temp = self.temporal_projection(x_temp)
+        x = time_to_feature(x_temp)
+
+        return x
+    
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs = self(inputs)
+        return outputs.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/units.py b/probts/model/forecaster/point_forecaster/units.py
deleted file mode 100644
index 10dc6d7..0000000
--- a/probts/model/forecaster/point_forecaster/units.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from UniTS
-# - Source: https://github.com/mims-harvard/UniTS
-# - Paper: UNITS: A Unified Multi-Task Time Series Model
-# - License: MIT License
-#
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import math
-
-import torch
-import torch.nn.functional as F
-from timm.layers import DropPath, Mlp
-from timm.layers.helpers import to_2tuple
-from torch import nn
-
-from probts.model.forecaster import Forecaster
-
-
-def calculate_unfold_output_length(input_length, size, step):
-    # Calculate the number of windows
-    num_windows = (input_length - size) // step + 1
-    return num_windows
-
-
-class CrossAttention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=False,
-            qk_norm=False,
-            attn_drop=0.,
-            proj_drop=0.,
-            norm_layer=nn.LayerNorm,
-            var_num=None,
-    ):
-        super().__init__()
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-
-        self.q = nn.Linear(dim, dim, bias=qkv_bias)
-        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
-        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        if var_num is not None:
-            self.template = nn.Parameter(
-                torch.zeros(var_num, dim), requires_grad=True)
-            torch.nn.init.normal_(self.template, std=.02)
-        self.var_num = var_num
-
-    def forward(self, x, query=None):
-        B, N, C = x.shape
-        if query is not None:
-            q = self.q(query).reshape(
-                B, query.shape[1], self.num_heads, self.head_dim).permute(0, 2, 1, 3)
-            q = self.q_norm(q)
-            var_num = query.shape[1]
-        else:
-            q = self.q(self.template).reshape(1, self.var_num,
-                                              self.num_heads, self.head_dim).permute(0, 2, 1, 3)
-            q = self.q_norm(q)
-            q = q.repeat(B, 1, 1, 1)
-            var_num = self.var_num
-        kv = self.kv(x).reshape(B, N, 2, self.num_heads,
-                                self.head_dim).permute(2, 0, 3, 1, 4)
-        k, v = kv.unbind(0)
-        k = self.k_norm(k)
-
-        x = F.scaled_dot_product_attention(
-            q, k, v,
-            dropout_p=self.attn_drop.p if self.training else 0.,
-        )
-
-        x = x.transpose(1, 2).reshape(B, var_num, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class DynamicLinear(nn.Module):
-    """
-    A dynamic linear layer that can interpolate the weight size to support any given input and output feature dimension.
-    """
-
-    def __init__(self, in_features=None, out_features=None, fixed_in=0, bias=True):
-        super(DynamicLinear, self).__init__()
-        assert fixed_in < in_features, "fixed_in < in_features is required !!!"
-        self.in_features = in_features
-        self.out_features = out_features
-        self.weights = nn.Parameter(torch.Tensor(out_features, in_features))
-        self.bias = nn.Parameter(torch.Tensor(out_features))
-        self.fixed_in = fixed_in
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.kaiming_uniform_(self.weights, a=math.sqrt(5))
-        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weights)
-        bound = 1 / math.sqrt(fan_in)
-        nn.init.uniform_(self.bias, -bound, bound)
-
-    def forward(self, x, out_features):
-        """
-        Forward pass for the dynamic linear layer.
-        """
-        fixed_weights = self.weights[:, :self.fixed_in]
-        dynamic_weights = self.weights[:, self.fixed_in:]
-        this_bias = self.bias
-        in_features = x.shape[-1]
-
-        if in_features != self.weights.size(1) or out_features != self.weights.size(0):
-            dynamic_weights = F.interpolate(dynamic_weights.unsqueeze(0).unsqueeze(0), size=(
-                out_features, in_features-self.fixed_in), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
-            if self.fixed_in != 0:
-                fixed_weights = F.interpolate(fixed_weights.unsqueeze(0).unsqueeze(0), size=(
-                    out_features, self.fixed_in), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
-        if out_features != self.weights.size(0):
-            this_bias = F.interpolate(this_bias.unsqueeze(0).unsqueeze(0).unsqueeze(0), size=(
-                1, out_features), mode='bilinear', align_corners=False).squeeze(0).squeeze(0).squeeze(0)
-        return F.linear(x, torch.cat((fixed_weights, dynamic_weights), dim=1), this_bias)
-
-
-class DynamicLinearMlp(nn.Module):
-    def __init__(
-            self,
-            in_features,
-            hidden_features=None,
-            out_features=None,
-            act_layer=nn.GELU,
-            norm_layer=None,
-            bias=True,
-            drop=0.,
-            prefix_token_length=None,
-            group=1,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        bias = to_2tuple(bias)
-        drop_probs = to_2tuple(drop)
-
-        self.fc1 = nn.Conv1d(in_features, hidden_features,
-                             3, groups=group, bias=bias[0], padding=1)
-        self.act = act_layer()
-        self.drop1 = nn.Dropout(drop_probs[0])
-
-        self.norm = norm_layer(
-            hidden_features) if norm_layer is not None else nn.Identity()
-        self.seq_fc = DynamicLinear(
-            hidden_features//4, hidden_features//4, bias=bias[1], fixed_in=prefix_token_length)
-        self.prompt_fc = DynamicLinear(
-            hidden_features//4, prefix_token_length, bias=bias[1], fixed_in=prefix_token_length)
-
-        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
-        self.drop2 = nn.Dropout(drop_probs[1])
-        self.hidden_features = hidden_features
-        self.prefix_token_length = prefix_token_length
-
-    def dynamic_linear(self, x, prefix_seq_len):
-        x_func = x[:, :, prefix_seq_len:]
-        x_seq = x[:, :, :prefix_seq_len]
-        x_seq_out = self.seq_fc(
-            x_seq, x_seq.shape[-1]-self.prefix_token_length)
-        x_prompt = self.prompt_fc(x_seq, self.prefix_token_length)
-        x = torch.cat((x_prompt, x_seq_out, x_func), dim=-1)
-        return x
-
-    def split_dynamic_linear(self, x, prefix_seq_len):
-        x1, x2 = x.chunk(2, dim=-2)
-        x1 = self.dynamic_linear(x1, prefix_seq_len)
-        return torch.cat((x1, x2), dim=-2)
-
-    def forward(self, x, prefix_seq_len, dim=2):
-        n, var, l, c = x.shape
-        x = x.view(-1, l, c)
-        x = x.transpose(-1, -2)
-        x = self.fc1(x)
-        x = self.split_dynamic_linear(x, prefix_seq_len)
-        x = self.act(x)
-        x = self.drop1(x)
-        x = x.transpose(1, 2)
-        x = self.norm(x)
-        x = self.fc2(x).view(n, var, l, c)
-        x = self.drop2(x)
-        return x
-
-
-class LearnablePositionalEmbedding(nn.Module):
-    def __init__(self, d_model, max_len=5000):
-        super(LearnablePositionalEmbedding, self).__init__()
-        # Compute the positional encodings once in log space.
-        self.pe = nn.Parameter(torch.zeros(
-            1, 1, max_len, d_model), requires_grad=True)
-
-        pe = torch.zeros(max_len, d_model).float()
-        position = torch.arange(0, max_len).float().unsqueeze(1)
-        div_term = (torch.arange(0, d_model, 2).float()
-                    * -(math.log(10000.0) / d_model)).exp()
-
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-
-        pe = pe.unsqueeze(0).unsqueeze(0)
-        self.pe.data.copy_(pe.float())
-        del pe
-
-    def forward(self, x, offset=0):
-        return self.pe[:, :, offset:offset+x.size(2)]
-
-
-class SeqAttention(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=False,
-            qk_norm=False,
-            attn_drop=0.,
-            proj_drop=0.,
-            norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, attn_mask=None):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
-                                  self.head_dim).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv.unbind(0)
-        q, k = self.q_norm(q), self.k_norm(k)
-        x = F.scaled_dot_product_attention(
-            q, k, v,  # attn_mask=attn_mask,
-            dropout_p=self.attn_drop.p if self.training else 0.,
-        )
-
-        x = x.transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class VarAttention(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=False,
-            qk_norm=False,
-            attn_drop=0.,
-            proj_drop=0.,
-            norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, P, C = x.shape
-
-        qkv = self.qkv(x).reshape(B, N, P, 3, self.num_heads,
-                                  self.head_dim).permute(3, 0, 2, 4, 1, 5)
-        q, k, v = qkv.unbind(0)
-        q, k = self.q_norm(q), self.k_norm(k)
-
-        q = q.mean(dim=1, keepdim=False)
-        k = k.mean(dim=1, keepdim=False)
-        v = v.permute(0, 2, 3, 4, 1).reshape(B, self.num_heads, N, -1)
-
-        x = F.scaled_dot_product_attention(
-            q, k, v,
-            dropout_p=self.attn_drop.p if self.training else 0.,
-        )
-
-        x = x.view(B, self.num_heads, N, -1, P).permute(0,
-                                                        2, 4, 1, 3).reshape(B, N, P, -1)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class GateLayer(nn.Module):
-    def __init__(self, dim, init_values=1e-5, inplace=False):
-        super().__init__()
-        self.inplace = inplace
-        self.gate = nn.Linear(dim, 1)
-
-    def forward(self, x):
-        gate_value = self.gate(x)
-        return gate_value.sigmoid() * x
-
-
-class SeqAttBlock(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            num_heads,
-            qkv_bias=False,
-            qk_norm=False,
-            proj_drop=0.,
-            attn_drop=0.,
-            init_values=None,
-            drop_path=0.,
-            norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn_seq = SeqAttention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_norm=qk_norm,
-            attn_drop=attn_drop,
-            proj_drop=proj_drop,
-            norm_layer=norm_layer,
-        )
-
-        self.ls1 = GateLayer(dim, init_values=init_values)
-        self.drop_path1 = DropPath(
-            drop_path) if drop_path > 0. else nn.Identity()
-        self.proj = nn.Linear(dim, dim)
-
-    def forward(self, x, attn_mask):
-        x_input = x
-        x = self.norm1(x)
-        n_vars, n_seqs = x.shape[1], x.shape[2]
-        x = torch.reshape(
-            x, (-1, x.shape[-2], x.shape[-1]))
-        x = self.attn_seq(x, attn_mask)
-        x = torch.reshape(
-            x, (-1, n_vars, n_seqs, x.shape[-1]))
-        x = x_input + self.drop_path1(self.ls1(x))
-        return x
-
-
-class VarAttBlock(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            num_heads,
-            qkv_bias=False,
-            qk_norm=False,
-            proj_drop=0.,
-            attn_drop=0.,
-            init_values=None,
-            drop_path=0.,
-            norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn_var = VarAttention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_norm=qk_norm,
-            attn_drop=attn_drop,
-            proj_drop=proj_drop,
-            norm_layer=norm_layer,
-        )
-        self.ls1 = GateLayer(dim, init_values=init_values)
-        self.drop_path1 = DropPath(
-            drop_path) if drop_path > 0. else nn.Identity()
-        self.proj = nn.Linear(dim, dim)
-
-    def forward(self, x):
-        x = x + self.drop_path1(self.ls1(self.attn_var(self.norm1(x))))
-        return x
-
-
-class MLPBlock(nn.Module):
-
-    def __init__(
-            self,
-            dim,
-            mlp_ratio=4.,
-            proj_drop=0.,
-            init_values=None,
-            drop_path=0.,
-            act_layer=nn.GELU,
-            norm_layer=nn.LayerNorm,
-            mlp_layer=None,
-            prefix_token_length=0,
-    ):
-        super().__init__()
-        self.norm2 = norm_layer(dim)
-        if mlp_layer is DynamicLinearMlp:
-            self.mlp = mlp_layer(
-                in_features=dim,
-                hidden_features=int(dim * mlp_ratio),
-                act_layer=act_layer,
-                drop=proj_drop,
-                prefix_token_length=prefix_token_length,
-            )
-        else:
-            self.mlp = mlp_layer(
-                in_features=dim,
-                hidden_features=int(dim * mlp_ratio),
-                act_layer=act_layer,
-                drop=proj_drop,
-            )
-        self.ls2 = GateLayer(dim, init_values=init_values)
-        self.drop_path2 = DropPath(
-            drop_path) if drop_path > 0. else nn.Identity()
-
-    def forward(self, x, prefix_seq_len=None):
-        if prefix_seq_len is not None:
-            x = x + \
-                self.drop_path2(
-                    self.ls2(self.mlp(self.norm2(x), prefix_seq_len=prefix_seq_len)))
-        else:
-            x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
-        return x
-
-
-class BasicBlock(nn.Module):
-    def __init__(
-            self,
-            dim,
-            num_heads,
-            mlp_ratio=8.,
-            qkv_bias=False,
-            qk_norm=False,
-            proj_drop=0.,
-            attn_drop=0.,
-            init_values=None,
-            drop_path=0.,
-            act_layer=nn.GELU,
-            norm_layer=nn.LayerNorm,
-            prefix_token_length=0,
-    ):
-        super().__init__()
-        self.seq_att_block = SeqAttBlock(dim=dim, num_heads=num_heads,
-                                         qkv_bias=qkv_bias, qk_norm=qk_norm,
-                                         attn_drop=attn_drop, init_values=init_values, proj_drop=proj_drop,
-                                         drop_path=drop_path, norm_layer=norm_layer)
-
-        self.var_att_block = VarAttBlock(dim=dim, num_heads=num_heads,
-                                         qkv_bias=qkv_bias, qk_norm=qk_norm,
-                                         attn_drop=attn_drop, init_values=init_values, proj_drop=proj_drop,
-                                         drop_path=drop_path, norm_layer=norm_layer)
-
-        self.dynamic_mlp = MLPBlock(dim=dim, mlp_ratio=mlp_ratio, mlp_layer=DynamicLinearMlp,
-                                    proj_drop=proj_drop, init_values=init_values, drop_path=drop_path,
-                                    act_layer=act_layer, norm_layer=norm_layer,
-                                    prefix_token_length=prefix_token_length)
-
-    def forward(self, x, prefix_seq_len, attn_mask):
-        x = self.seq_att_block(x, attn_mask)
-        x = self.var_att_block(x)
-        x = self.dynamic_mlp(x, prefix_seq_len=prefix_seq_len)
-        return x
-
-
-class PatchEmbedding(nn.Module):
-    def __init__(self, d_model, patch_len, stride, padding, dropout):
-        super(PatchEmbedding, self).__init__()
-        # Patching
-        self.patch_len = patch_len
-        self.stride = stride
-        assert self.patch_len == self.stride, "non-overlap"
-        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        n_vars = x.shape[1]
-        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
-        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
-        x = self.value_embedding(x)
-        return self.dropout(x), n_vars
-
-
-class CLSHead(nn.Module):
-    def __init__(self, d_model, head_dropout=0):
-        super().__init__()
-        d_mid = d_model
-        self.proj_in = nn.Linear(d_model, d_mid)
-        self.cross_att = CrossAttention(d_mid)
-
-        self.mlp = MLPBlock(dim=d_mid, mlp_ratio=8, mlp_layer=Mlp,
-                            proj_drop=head_dropout, init_values=None, drop_path=0.0,
-                            act_layer=nn.GELU, norm_layer=nn.LayerNorm,
-                            prefix_token_length=None)
-
-    def forward(self, x, category_token=None, return_feature=False):
-        x = self.proj_in(x)
-        B, V, L, C = x.shape
-        x = x.view(-1, L, C)
-        cls_token = x[:, -1:]
-        cls_token = self.cross_att(x, query=cls_token)
-        cls_token = cls_token.reshape(B, V, -1, C)
-
-        cls_token = self.mlp(cls_token)
-        if return_feature:
-            return cls_token
-        m = category_token.shape[2]
-        cls_token = cls_token.expand(B, V, m, C)
-        distance = torch.einsum('nvkc,nvmc->nvm', cls_token, category_token)
-
-        distance = distance.mean(dim=1)
-        return distance
-
-
-class ForecastHead(nn.Module):
-    def __init__(self, d_model, patch_len, stride, pad, head_dropout=0, prefix_token_length=None):
-        super().__init__()
-        d_mid = d_model
-        self.proj_in = nn.Linear(d_model, d_mid)
-        self.mlp = Mlp(
-            in_features=d_model,
-            hidden_features=int(d_model * 4),
-            act_layer=nn.GELU,
-            drop=head_dropout,
-        )
-        self.proj_out = nn.Linear(d_model, patch_len)
-        self.pad = pad
-        self.patch_len = patch_len
-        self.stride = stride
-        self.pos_proj = DynamicLinear(
-            in_features=128, out_features=128, fixed_in=prefix_token_length)
-
-    def forward(self, x_full, pred_len, token_len):
-        x_full = self.proj_in(x_full)
-        x_pred = x_full[:, :, -token_len:]
-        x = x_full.transpose(-1, -2)
-        x = self.pos_proj(x, token_len)
-        x = x.transpose(-1, -2)
-        x = x + x_pred
-        x = self.mlp(x)
-        x = self.proj_out(x)
-
-        bs, n_vars = x.shape[0], x.shape[1]
-        x = x.reshape(-1, x.shape[-2], x.shape[-1])
-        x = x.permute(0, 2, 1)
-        x = torch.nn.functional.fold(x, output_size=(
-            pred_len, 1), kernel_size=(self.patch_len, 1), stride=(self.stride, 1))
-        x = x.squeeze(dim=-1)
-        x = x.reshape(bs, n_vars, -1)
-        x = x.permute(0, 2, 1)
-        return x
-
-
-class Model(nn.Module):
-    """
-    UniTS: Building a Unified Time Series Model
-    """
-
-    def __init__(self, args, configs_list, pretrain=False):
-        super().__init__()
-
-        # (zhenwei) we do not pretrain the model in this stage
-        # if pretrain:
-        #     self.right_prob = args.right_prob
-        #     self.min_mask_ratio = args.min_mask_ratio
-        #     self.max_mask_ratio = args.max_mask_ratio
-
-        # Tokens settings
-        self.num_task = len(configs_list)
-        self.prompt_tokens = nn.ParameterDict({})
-        self.mask_tokens = nn.ParameterDict({})
-        self.cls_tokens = nn.ParameterDict({})
-        self.category_tokens = nn.ParameterDict({})
-
-        for i in range(self.num_task):
-            dataset_name = configs_list[i][1]['dataset']
-            task_data_name = configs_list[i][0]
-            if dataset_name not in self.prompt_tokens:
-                self.prompt_tokens[dataset_name] = torch.zeros(
-                    1, configs_list[i][1]['enc_in'], args.prompt_num, args.d_model)
-                torch.nn.init.normal_(
-                    self.prompt_tokens[dataset_name], std=.02)
-                self.mask_tokens[dataset_name] = torch.zeros(
-                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
-
-            if configs_list[i][1]['task_name'] == 'classification':
-                self.category_tokens[task_data_name] = torch.zeros(
-                    1, configs_list[i][1]['enc_in'], configs_list[i][1]['num_class'], args.d_model)
-                torch.nn.init.normal_(
-                    self.category_tokens[task_data_name], std=.02)
-                self.cls_tokens[task_data_name] = torch.zeros(
-                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
-                torch.nn.init.normal_(self.cls_tokens[task_data_name], std=.02)
-            if pretrain:
-                self.cls_tokens[task_data_name] = torch.zeros(
-                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
-                torch.nn.init.normal_(self.cls_tokens[task_data_name], std=.02)
-
-        self.cls_nums = {}
-        for i in range(self.num_task):
-            task_data_name = configs_list[i][0]
-            if configs_list[i][1]['task_name'] == 'classification':
-                self.cls_nums[task_data_name] = configs_list[i][1]['num_class']
-            elif configs_list[i][1]['task_name'] == 'long_term_forecast':
-                remainder = configs_list[i][1]['seq_len'] % args.patch_len
-                if remainder == 0:
-                    padding = 0
-                else:
-                    padding = args.patch_len - remainder
-                input_token_len = calculate_unfold_output_length(
-                    configs_list[i][1]['seq_len']+padding, args.stride, args.patch_len)
-                input_pad = args.stride * \
-                    (input_token_len - 1) + args.patch_len - \
-                    configs_list[i][1]['seq_len']
-                pred_token_len = calculate_unfold_output_length(
-                    configs_list[i][1]['pred_len']-input_pad, args.stride, args.patch_len)
-                real_len = configs_list[i][1]['seq_len'] + \
-                    configs_list[i][1]['pred_len']
-                self.cls_nums[task_data_name] = [pred_token_len,
-                                                 configs_list[i][1]['pred_len'], real_len]
-
-        self.configs_list = configs_list
-
-        ### model settings ###
-        self.prompt_num = args.prompt_num
-        self.stride = args.stride
-        self.pad = args.stride
-        self.patch_len = args.patch_len
-
-        # input processing
-        self.patch_embeddings = PatchEmbedding(
-            args.d_model, args.patch_len, args.stride, args.stride, args.dropout)
-        self.position_embedding = LearnablePositionalEmbedding(args.d_model)
-        self.prompt2forecat = DynamicLinear(128, 128, fixed_in=args.prompt_num)
-
-        # basic blocks
-        self.block_num = args.e_layers
-        self.blocks = nn.ModuleList(
-            [BasicBlock(dim=args.d_model, num_heads=args.n_heads, qkv_bias=False, qk_norm=False,
-                        mlp_ratio=8., proj_drop=args.dropout, attn_drop=0., drop_path=0.,
-                        init_values=None, prefix_token_length=args.prompt_num) for l in range(args.e_layers)]
-        )
-
-        # output processing
-        self.cls_head = CLSHead(args.d_model, head_dropout=args.dropout)
-        self.forecast_head = ForecastHead(
-            args.d_model, args.patch_len, args.stride, args.stride, prefix_token_length=args.prompt_num, head_dropout=args.dropout)
-        if pretrain:
-            self.pretrain_head = ForecastHead(
-                args.d_model, args.patch_len, args.stride, args.stride, prefix_token_length=1, head_dropout=args.dropout)
-
-    def tokenize(self, x, mask=None):
-        # Normalization from Non-stationary Transformer
-        means = x.mean(1, keepdim=True).detach()
-        x = x - means
-        if mask is not None:
-            x = x.masked_fill(mask == 0, 0)
-            stdev = torch.sqrt(torch.sum(x * x, dim=1) /
-                               torch.sum(mask == 1, dim=1) + 1e-5)
-            stdev = stdev.unsqueeze(dim=1)
-        else:
-            stdev = torch.sqrt(
-                torch.var(x, dim=1, keepdim=True, unbiased=False) + 1e-5)
-        x /= stdev
-        x = x.permute(0, 2, 1)
-        remainder = x.shape[2] % self.patch_len
-        if remainder != 0:
-            padding = self.patch_len - remainder
-            x = F.pad(x, (0, padding))
-        else:
-            padding = 0
-        x, n_vars = self.patch_embeddings(x)
-        return x, means, stdev, n_vars, padding
-
-    def prepare_prompt(self, x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name=None, mask=None):
-        x = torch.reshape(
-            x, (-1, n_vars, x.shape[-2], x.shape[-1]))
-        # append prompt tokens
-        this_prompt = prefix_prompt.repeat(x.shape[0], 1, 1, 1)
-
-        if task_name == 'forecast':
-            this_mask_prompt = task_prompt.repeat(
-                x.shape[0], 1, task_prompt_num, 1)
-            init_full_input = torch.cat(
-                (this_prompt, x, this_mask_prompt), dim=-2)
-            init_mask_prompt = self.prompt2forecat(init_full_input.transpose(
-                -1, -2), init_full_input.shape[2]-prefix_prompt.shape[2]).transpose(-1, -2)
-            this_function_prompt = init_mask_prompt[:, :, -task_prompt_num:]
-            x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
-            x[:, :, self.prompt_num:] = x[:, :, self.prompt_num:] + \
-                self.position_embedding(x[:, :, self.prompt_num:])
-        elif task_name == 'classification':
-            this_function_prompt = task_prompt.repeat(x.shape[0], 1, 1, 1)
-            x = x + self.position_embedding(x)
-            x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
-        elif task_name == 'imputation':
-            # fill the masked parts with mask tokens
-            # for imputation, masked is 0, unmasked is 1, so here to reverse mask
-            mask = 1-mask
-            mask = mask.permute(0, 2, 1)
-            mask = self.mark2token(mask)
-            mask_repeat = mask.unsqueeze(dim=-1)
-
-            mask_token = task_prompt
-            mask_repeat = mask_repeat.repeat(1, 1, 1, x.shape[-1])
-            x = x * (1-mask_repeat) + mask_token * mask_repeat
-
-            init_full_input = torch.cat((this_prompt, x), dim=-2)
-            init_mask_prompt = self.prompt2forecat(
-                init_full_input.transpose(-1, -2), x.shape[2]).transpose(-1, -2)
-            # keep the unmasked tokens and fill the masked ones with init_mask_prompt.
-            x = x * (1-mask_repeat) + init_mask_prompt * mask_repeat
-            x = x + self.position_embedding(x)
-            x = torch.cat((this_prompt, x), dim=2)
-        elif task_name == 'anomaly_detection':
-            x = x + self.position_embedding(x)
-            x = torch.cat((this_prompt, x), dim=2)
-
-        return x
-
-    def mark2token(self, x_mark):
-        x_mark = x_mark.unfold(
-            dimension=-1, size=self.patch_len, step=self.stride)
-        x_mark = x_mark.mean(dim=-1)
-        x_mark = (x_mark > 0).float()
-        return x_mark
-
-    def backbone(self, x, prefix_len, seq_len):
-        attn_mask = None
-        for block in self.blocks:
-            x = block(x, prefix_seq_len=prefix_len +
-                      seq_len, attn_mask=attn_mask)
-        return x
-
-    def forecast(self, x, x_mark, task_id):
-        dataset_name = self.configs_list[task_id][1]['dataset']
-        task_data_name = self.configs_list[task_id][0]
-        prefix_prompt = self.prompt_tokens[dataset_name]
-        task_prompt = self.mask_tokens[dataset_name]
-        task_prompt_num = self.cls_nums[task_data_name][0]
-        task_seq_num = self.cls_nums[task_data_name][1]
-        real_seq_len = self.cls_nums[task_data_name][2]
-
-        x, means, stdev, n_vars, _ = self.tokenize(x)
-
-        x = self.prepare_prompt(
-            x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name='forecast')
-
-        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
-        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
-
-        x = self.forecast_head(
-            x, real_seq_len, seq_token_len)
-        x = x[:, -task_seq_num:]
-
-        # De-Normalization from Non-stationary Transformer
-        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-
-        return x
-
-    def classification(self, x, x_mark, task_id):
-        dataset_name = self.configs_list[task_id][1]['dataset']
-        task_data_name = self.configs_list[task_id][0]
-        prefix_prompt = self.prompt_tokens[dataset_name]
-        task_prompt = self.cls_tokens[task_data_name]
-        task_prompt_num = 1
-        category_token = self.category_tokens[task_data_name]
-
-        x, means, stdev, n_vars, _ = self.tokenize(x)
-
-        seq_len = x.shape[-2]
-
-        x = self.prepare_prompt(
-            x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name='classification')
-
-        x = self.backbone(x, prefix_prompt.shape[2], seq_len)
-
-        x = self.cls_head(x, category_token)
-
-        return x
-
-    def imputation(self, x, x_mark, mask, task_id):
-        dataset_name = self.configs_list[task_id][1]['dataset']
-        prefix_prompt = self.prompt_tokens[dataset_name]
-        task_prompt = self.mask_tokens[dataset_name]
-
-        seq_len = x.shape[1]
-        x, means, stdev, n_vars, padding = self.tokenize(x, mask)
-
-        x = self.prepare_prompt(
-            x, n_vars, prefix_prompt, task_prompt, None, mask=mask, task_name='imputation')
-        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
-        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
-
-        x = self.forecast_head(
-            x, seq_len+padding, seq_token_len)
-        x = x[:, :seq_len]
-
-        # De-Normalization from Non-stationary Transformer
-        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-
-        return x
-
-    def anomaly_detection(self, x, x_mark, task_id):
-        dataset_name = self.configs_list[task_id][1]['dataset']
-        prefix_prompt = self.prompt_tokens[dataset_name]
-
-        seq_len = x.shape[1]
-        x, means, stdev, n_vars, padding = self.tokenize(x)
-
-        x = self.prepare_prompt(x, n_vars, prefix_prompt,
-                                None, None, task_name='anomaly_detection')
-        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
-        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
-
-        x = self.forecast_head(
-            x, seq_len+padding, seq_token_len)
-        x = x[:, :seq_len]
-
-        # De-Normalization from Non-stationary Transformer
-        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
-
-        return x
-
-    def random_masking(self, x, min_mask_ratio, max_mask_ratio):
-        """
-        Perform per-sample random masking.
-        """
-        N, V, L, D = x.shape  # batch, var, length, dim
-
-        # Calculate mask ratios and lengths to keep for each sample in the batch
-        mask_ratios = torch.rand(N, device=x.device) * \
-            (max_mask_ratio - min_mask_ratio) + min_mask_ratio
-        len_keeps = (L * (1 - mask_ratios)).long()
-
-        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
-
-        # sort noise for each sample
-        # ascend: small is keep, large is remove
-        ids_shuffle = torch.argsort(noise, dim=1)
-        ids_restore = torch.argsort(ids_shuffle, dim=1)
-
-        # generate the binary mask: 0 is keep, 1 is remove
-        mask = torch.ones([N, L], device=x.device)
-
-        # Create a range tensor and compare with len_keeps for mask generation
-        range_tensor = torch.arange(L, device=x.device).expand(N, L)
-        mask = (range_tensor >= len_keeps.unsqueeze(1))
-
-        # unshuffle to get the binary mask
-        mask = torch.gather(mask, dim=1, index=ids_restore)
-        mask = mask.float()
-
-        return mask
-
-    def right_masking(self, x, min_mask_ratio, max_mask_ratio):
-        N, V, L, D = x.shape  # batch, var, length, dim
-
-        # Randomly choose a mask ratio for each sample within the specified range
-        mask_ratios = torch.rand(N, device=x.device) * \
-            (max_mask_ratio - min_mask_ratio) + min_mask_ratio
-        len_keeps = (L * (1 - mask_ratios)).long()
-
-        # Binary mask creation without a for loop
-        len_keeps_matrix = len_keeps.unsqueeze(1).expand(N, L)
-        indices = torch.arange(L, device=x.device).expand_as(len_keeps_matrix)
-        mask = indices >= len_keeps_matrix
-        mask = mask.float()
-
-        return mask
-
-    def choose_masking(self, x, right_prob, min_mask_ratio, max_mask_ratio):
-        # Generate a random number to decide which masking function to use
-        if torch.rand(1).item() > right_prob:
-            return self.random_masking(x, min_mask_ratio, max_mask_ratio)
-        else:
-            return self.right_masking(x, min_mask_ratio, max_mask_ratio)
-
-    def get_mask_seq(self, mask, seq_len):
-        mask_seq = mask.unsqueeze(dim=-1).repeat(1, 1, self.patch_len)
-        mask_seq = mask_seq.permute(0, 2, 1)
-        mask_seq = mask_seq.masked_fill(mask_seq == 0, -1e9)
-        # Fold operation
-        mask_seq = torch.nn.functional.fold(mask_seq, output_size=(
-            seq_len, 1), kernel_size=(self.patch_len, 1), stride=(self.stride, 1))
-        # Apply threshold to bring back to 0/1 values
-        mask_seq = (mask_seq > 0).float()
-        mask_seq = mask_seq.squeeze(dim=-1).squeeze(dim=1)
-        return mask_seq
-
-    def pretraining(self, x, x_mark, task_id, enable_mask=False):
-        dataset_name = self.configs_list[task_id][1]['dataset']
-        task_data_name = self.configs_list[task_id][0]
-        prefix_prompt = self.prompt_tokens[dataset_name]
-        mask_token = self.mask_tokens[dataset_name]
-        cls_token = self.cls_tokens[task_data_name]
-
-        seq_len = x.shape[1]
-        x, means, stdev, n_vars, padding = self.tokenize(x)
-        seq_token_len = x.shape[-2]
-
-        # append prompt tokens
-        x = torch.reshape(
-            x, (-1, n_vars, x.shape[-2], x.shape[-1]))
-        # prepare prompts
-        this_prompt = prefix_prompt.repeat(x.shape[0], 1, 1, 1)
-
-        if enable_mask:
-            mask = self.choose_masking(x, self.right_prob,
-                                       self.min_mask_ratio, self.max_mask_ratio)
-            mask_repeat = mask.unsqueeze(dim=1).unsqueeze(dim=-1)
-            mask_repeat = mask_repeat.repeat(1, x.shape[1], 1, x.shape[-1])
-            x = x * (1-mask_repeat) + mask_token * mask_repeat  # todo
-
-            init_full_input = torch.cat((this_prompt, x), dim=-2)
-            init_mask_prompt = self.prompt2forecat(
-                init_full_input.transpose(-1, -2), x.shape[2]).transpose(-1, -2)
-            # keep the unmasked tokens and fill the masked ones with init_mask_prompt.
-            x = x * (1-mask_repeat) + init_mask_prompt * mask_repeat
-            x = x + self.position_embedding(x)
-            mask_seq = self.get_mask_seq(mask, seq_len+padding)
-            mask_seq = mask_seq[:, :seq_len]
-        this_function_prompt = cls_token.repeat(x.shape[0], 1, 1, 1)
-        x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
-
-        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
-
-        if enable_mask:
-            mask_dec_out = self.forecast_head(
-                x[:, :, :-1], seq_len+padding, seq_token_len)
-            mask_dec_out = mask_dec_out[:, :seq_len]
-            # De-Normalization from Non-stationary Transformer
-            mask_dec_out = mask_dec_out * \
-                (stdev[:, 0, :].unsqueeze(1).repeat(
-                    1, mask_dec_out.shape[1], 1))
-            mask_dec_out = mask_dec_out + \
-                (means[:, 0, :].unsqueeze(1).repeat(
-                    1, mask_dec_out.shape[1], 1))
-            cls_dec_out = self.cls_head(x, return_feature=True)
-            # detach grad of the forecasting on tokens
-            fused_dec_out = torch.cat(
-                (cls_dec_out, x[:, :, self.prompt_num:-1].detach()), dim=2)
-            cls_dec_out = self.pretrain_head(
-                fused_dec_out, seq_len+padding, seq_token_len)
-            cls_dec_out = cls_dec_out[:, :seq_len]
-            cls_dec_out = cls_dec_out * \
-                (stdev[:, 0, :].unsqueeze(1).repeat(
-                    1, cls_dec_out.shape[1], 1))
-            cls_dec_out = cls_dec_out + \
-                (means[:, 0, :].unsqueeze(1).repeat(
-                    1, cls_dec_out.shape[1], 1))
-
-            return cls_dec_out, mask_dec_out, mask_seq
-        else:
-            return cls_dec_out
-
-    def forward(self, x_enc, x_mark_enc, x_dec=None, x_mark_dec=None,
-                mask=None, task_id=None, task_name=None, enable_mask=None):
-        task_id = 0
-
-        # if task_name == 'long_term_forecast' or task_name == 'short_term_forecast':
-        dec_out = self.forecast(x_enc, x_mark_enc, task_id)
-        return dec_out  # [B, L, D]
-        # if task_name == 'imputation':
-        #     dec_out = self.imputation(
-        #         x_enc, x_mark_enc, mask, task_id)
-        #     return dec_out  # [B, L, D]
-        # if task_name == 'anomaly_detection':
-        #     dec_out = self.anomaly_detection(x_enc, x_mark_enc, task_id)
-        #     return dec_out  # [B, L, D]
-        # if task_name == 'classification':
-        #     dec_out = self.classification(x_enc, x_mark_enc, task_id)
-        #     return dec_out  # [B, N]
-        # if 'pretrain' in task_name:
-        #     dec_out = self.pretraining(x_enc, x_mark_enc, task_id,
-        #                                enable_mask=enable_mask)
-        #     return dec_out
-        # return None
-
-
-class UniTS(Forecaster):
-    def __init__(
-        self,
-        ckpt_path: str = None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.no_training = True
-
-        args, configs_list = self.generate_units_default_args(self.dataset)
-        self.model = Model(args, configs_list, pretrain=False)
-        
-        pretrain_weight_path = ckpt_path
-
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        state_dict = torch.load(pretrain_weight_path, map_location=device)['student']
-        ckpt = {}
-        for k, v in state_dict.items():
-            if not ('cls_prompts' in k):
-                k = k.replace('module.', '') if 'module.' in k else k
-                ckpt[k] = v
-        
-        msg = self.model.load_state_dict(ckpt, strict=False)
-        if len(msg.missing_keys) > 0:
-            print(f"""Warning: There are missing keys in the pretrained model: {msg.missing_keys}, 
-                which may cause prediction results less accurate.""")
-
-
-    def generate_units_default_args(self, dataset_name='ETTh1'):
-        class Args:
-            def __init__(self):
-                self.d_model = 128
-                self.n_heads = 8
-                self.e_layers = 3
-                self.prompt_num = 10
-                self.dropout = 0.1
-                self.patch_len = 16
-                self.stride = 16
-                self.batch_size = 32
-
-        args = Args()
-
-        # parse dataset names - ECL, ETTh1, Exchange, ILI, Traffic, Weather
-        units_valid_dataset_map = {
-            'ECL': ['ECL', 'electricity'],
-            'ETTh1': ['ETT'],
-            'Exchange': ['Exchange'],
-            'ILI': ['ILI'],
-            'Traffic': ['Traffic'],
-            'Weather': ['Weather']
-        }
-
-        units_dataset_name = 'DEFAULT'
-        for key, value_list in units_valid_dataset_map.items():
-            if any(substring.lower() in dataset_name for substring in value_list):
-                units_dataset_name = key
-                break
-        task_name = f"LTF_{units_dataset_name}_p{self.prediction_length}"
-
-        task_data_config = {
-            task_name: {
-                "task_name": "long_term_forecast",
-                "dataset": units_dataset_name,
-                "data": units_dataset_name,
-                "embed": "timeF",
-                "features": "M",
-                "seq_len": self.context_length,
-                "label_len": 48,
-                "pred_len": self.prediction_length,
-                "enc_in": self.target_dim,
-                "dec_in": self.target_dim,
-                "c_out": self.target_dim
-            }
-        }
-        task_data_config_list = []
-        for task_name, task_config in task_data_config.items():
-            task_config['max_batch'] = args.batch_size
-            task_data_config_list.append([task_name, task_config])
-        return args, task_data_config_list
-
-
-    def forecast(self, batch_data, pred_len=None, dataset_name=None, *args, **kwargs):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = inputs[:, -self.context_length:]
-        B, _, K = inputs.shape
-        point_forecast = self.model.forward(inputs, None)
-        return point_forecast.unsqueeze(1)
diff --git a/probts/model/forecaster/prob_forecaster/__init__.py b/probts/model/forecaster/prob_forecaster/__init__.py
deleted file mode 100644
index 49c2d84..0000000
--- a/probts/model/forecaster/prob_forecaster/__init__.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from .gru_nvp import GRU_NVP
-from .gru_maf import GRU_MAF
-from .timegrad import TimeGrad
-from .trans_maf import Trans_MAF
-from .csdi import CSDI
-from .tsdiff import TSDiffCond
-
-# ------- add lag_llama to sys.path ---------
-try:
-    import os, sys
-    current_dir = os.path.dirname(os.path.realpath(__file__))
-    project_root = os.path.abspath(os.path.join(current_dir, '..', '..', '..', '..'))
-    lag_llama_path = os.path.join(project_root, 'submodules', 'lag_llama')
-    moirai_path = os.path.join(project_root, 'submodules', 'uni2ts', 'src')
-
-    if lag_llama_path not in sys.path:
-        sys.path.append(lag_llama_path)
-
-    if moirai_path not in sys.path:
-        sys.path.append(moirai_path)
-
-except Exception as e:
-    print(f"Warning: Unable to add lag_llama to sys.path. {e}")
-# -------------------------------------------
-
-import importlib
-
-modules = [
-    ('moirai', 'Moirai'),
-    ('chronos', 'Chronos'),
-    ('lag_llama', 'LagLlama'),
-]
-
-for module, class_name in modules:
-    try:
-        mod = importlib.import_module(f".{module}", package=__package__)
-        globals()[class_name] = getattr(mod, class_name)
-    except ImportError:
-        # print(f"Warning: {class_name} is not available due to missing dependencies.")
-        pass
\ No newline at end of file
diff --git a/probts/model/forecaster/prob_forecaster/chronos.py b/probts/model/forecaster/prob_forecaster/chronos.py
deleted file mode 100644
index af7214a..0000000
--- a/probts/model/forecaster/prob_forecaster/chronos.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from Chronos
-# - Source: https://github.com/amazon-science/chronos-forecasting
-# - Paper: Chronos: Learning the Language of Time Series
-# - License: Apache License 2.0
-#
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-from chronos import ChronosPipeline
-from einops import rearrange
-
-from probts.model.forecaster import Forecaster
-
-
-class Chronos(Forecaster):
-    def __init__(
-        self,
-        model_size: str = 'base',
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        
-        self.pred_len = kwargs.get('prediction_length')
-        print(self.context_length, self.pred_len)
-
-        # Load pretrained model
-        self.no_training = True
-        # Load Chronos
-        self.pipeline = ChronosPipeline.from_pretrained(
-            "amazon/chronos-t5-{}".format(model_size),
-            device_map="cuda",
-            torch_dtype=torch.bfloat16,
-        )
-
-
-    def forecast(self, batch_data, num_samples=None):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = inputs[:, -self.context_length:]
-        
-        B, _, K = inputs.shape
-        inputs = rearrange(inputs, 'b l k -> (b k) l').cpu()
-        context = [inputs[i] for i in range(B*K)]
-        inner_batch_size = 12 # for 80G gpu
-        forecast_samples = []
-
-        # Process in batches of size `inner_batch_size`
-        for i in range(0, len(context), inner_batch_size):
-            batch_context = context[i:i + inner_batch_size]
-            batch_forecast_samples = self.pipeline.predict(
-                batch_context,
-                prediction_length=self.pred_len,
-                num_samples=num_samples,
-                limit_prediction_length=False
-            )
-            forecast_samples.append(batch_forecast_samples)
-        
-        forecast_samples = torch.cat(forecast_samples, dim=0)
-        prob_forecast = rearrange(forecast_samples, '(b k) s l -> b s l k', b=B, k=K)
-        
-        return prob_forecast
-
-
diff --git a/probts/model/forecaster/prob_forecaster/csdi.py b/probts/model/forecaster/prob_forecaster/csdi.py
deleted file mode 100644
index 91042bc..0000000
--- a/probts/model/forecaster/prob_forecaster/csdi.py
+++ /dev/null
@@ -1,200 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from CSDI
-# - Source: https://github.com/ermongroup/CSDI
-# - Paper: CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
-# - License: MIT license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-from probts.model.forecaster import Forecaster
-from probts.model.nn.layers import diff_CSDI
-
-
-class CSDI(Forecaster):
-    def __init__(
-        self, 
-        channels: int = 64,
-        emb_time_dim: int = 128,
-        emb_feature_dim: int = 16,
-        num_steps: int = 50,
-        schedule: str = "quad",
-        beta_start: float = 0.0001,
-        beta_end: float = 0.5,
-        diffusion_embedding_dim: int = 128,
-        num_heads: int = 8,
-        n_layers: int = 4,
-        sample_size: int = 64,
-        linear_trans: bool = False,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.autoregressive = False
-        self.dist_args = nn.Identity()
-
-        self.emb_time_dim = emb_time_dim
-        self.emb_feature_dim = emb_feature_dim
-        self.emb_total_dim = self.emb_time_dim + self.emb_feature_dim
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        self.emb_total_dim += 1  # for conditional mask
-        self.embed_layer = nn.Embedding(
-            num_embeddings=self.target_dim, embedding_dim=self.emb_feature_dim
-        )
-        side_dim = self.emb_total_dim
-        self.sample_size = sample_size
-
-        input_dim = 2
-        self.diffmodel = diff_CSDI(channels, diffusion_embedding_dim, side_dim, num_steps, num_heads, n_layers, inputdim=input_dim,linear=linear_trans)
-
-        # parameters for diffusion models
-        self.num_steps = num_steps
-        if schedule == "quad":
-            self.beta = np.linspace(
-                beta_start ** 0.5, beta_end ** 0.5, self.num_steps
-            ) ** 2
-        elif schedule == "linear":
-            self.beta = np.linspace(
-                beta_start, beta_end, self.num_steps
-            )
-
-        self.alpha_hat = 1 - self.beta
-        self.alpha = np.cumprod(self.alpha_hat)
-        self.alpha_torch = torch.tensor(self.alpha).float().unsqueeze(1).unsqueeze(1).to(self.device)
-
-    def time_embedding(self, pos, device, d_model=128):
-        pe = torch.zeros(pos.shape[0], pos.shape[1], d_model).to(device)
-        position = pos.unsqueeze(2)
-        div_term = 1 / torch.pow(
-            10000.0, torch.arange(0, d_model, 2).to(device) / d_model
-        )
-        pe[:, :, 0::2] = torch.sin(position * div_term)
-        pe[:, :, 1::2] = torch.cos(position * div_term)
-        return pe
-
-    def set_input_to_diffmodel(self, noisy_data, observed_data, cond_mask):
-        cond_obs = (cond_mask * observed_data).unsqueeze(1)
-        noisy_target = ((1 - cond_mask) * noisy_data).unsqueeze(1)
-        total_input = torch.cat([cond_obs, noisy_target], dim=1)  # (B,2,K,L)
-        return total_input
-
-    def get_masks(self, batch_data):
-        hist_observed_mask = batch_data.past_observed_values[:, -self.context_length:, ...]
-        target_observed_mask = batch_data.future_observed_values
-        observed_mask = torch.cat((hist_observed_mask, target_observed_mask), dim=1)
-
-        cond_mask = torch.cat((hist_observed_mask, torch.zeros_like(target_observed_mask)), dim=1)
-        return observed_mask, cond_mask # [B L K]
-
-    def get_side_info(self, observed_data, cond_mask, target_dimension_indicator, observed_tp=None):
-        
-        B, K, L = observed_data.shape
-        if observed_tp is None:
-            observed_tp = torch.arange(L) * 1.0
-            observed_tp = repeat(observed_tp, 'l -> b l', b=B).to(observed_data.device)
-
-        time_embed = self.time_embedding(observed_tp, observed_data.device, self.emb_time_dim)  # (B,L,emb)
-        time_embed = time_embed.unsqueeze(2).expand(-1, -1, K, -1) # (B,L,K, emb)
-        feature_embed = self.embed_layer(target_dimension_indicator)  # (B, K,emb)
-        feature_embed = feature_embed.unsqueeze(1).expand(-1, L, -1, -1) # (B,L,K, emb)
-
-        side_info = torch.cat([time_embed, feature_embed], dim=-1)  # (B,L,K,*)
-        side_info = side_info.permute(0, 3, 2, 1)  # (B,*,K,L)
-        side_mask = cond_mask.unsqueeze(1)  # (B,1,K,L)
-
-        side_info = torch.cat([side_info, side_mask], dim=1)
-        return side_info # (B,D,K,L)
-
-    def loss(self, batch_data, observed_tp=None):
-        past_target_cdf = batch_data.past_target_cdf[:, -self.context_length:, ...]
-        future_target_cdf = batch_data.future_target_cdf
-
-        observed_data = torch.cat([past_target_cdf, future_target_cdf], dim=1)
-        B, L, K = observed_data.shape
-        t = torch.randint(0, self.num_steps, [B]).to(past_target_cdf.device)
-
-        observed_mask, gt_mask = self.get_masks(batch_data)
-        feature_id = batch_data.target_dimension_indicator
-
-        if K > self.sample_size:
-            # sample subset
-            sampled_data = []
-            sampled_mask = []
-            sampled_feature_id = []
-            sampled_gt_mask = []
-            for i in range(len(observed_data)):
-                ind = np.arange(K)
-                np.random.shuffle(ind)
-                sampled_data.append(observed_data[i,...,ind[:self.sample_size]])
-                sampled_mask.append(observed_mask[i,...,ind[:self.sample_size]])
-                sampled_feature_id.append(feature_id[i,ind[:self.sample_size]])
-                sampled_gt_mask.append(gt_mask[i,...,ind[:self.sample_size]])
-            observed_data = torch.stack(sampled_data,0)
-            observed_mask = torch.stack(sampled_mask,0)
-            feature_id = torch.stack(sampled_feature_id,0)
-            gt_mask = torch.stack(sampled_gt_mask,0)
-
-        observed_data = observed_data.permute(0,2,1) # [B K L]
-        observed_mask = observed_mask.permute(0,2,1) # [B K L]
-        cond_mask = gt_mask.permute(0,2,1) # [B K L]
-
-        side_info = self.get_side_info(observed_data, cond_mask, feature_id, observed_tp)
-
-        target_mask = observed_mask - cond_mask
-        current_alpha = self.alpha_torch[t]  # (B,1,1)
-        noise = torch.randn_like(observed_data).to(observed_data.device)
-        noisy_data = (current_alpha ** 0.5) * observed_data + (1.0 - current_alpha) ** 0.5 * noise
-
-
-        total_input = self.set_input_to_diffmodel(noisy_data, observed_data, cond_mask)
-
-        predicted = self.diffmodel(total_input, side_info, t)  # (B,K,L)
-        residual = (noise - predicted) * target_mask
-
-        num_eval = target_mask.sum()
-        loss = (residual ** 2).sum() / (num_eval if num_eval > 0 else 1)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples):
-        observed_data = torch.cat([batch_data.past_target_cdf[:, -self.context_length:, ...], torch.zeros_like(batch_data.future_target_cdf)], dim=1).permute(0,2,1) 
-        _, cond_mask = self.get_masks(batch_data)
-        cond_mask = cond_mask.permute(0,2,1)
-        side_info = self.get_side_info(observed_data, cond_mask, batch_data.target_dimension_indicator)
-        sample = self.sample(observed_data, cond_mask, side_info, num_samples)
-        sample = sample.permute(0,1,3,2)
-        return sample[:, : , -self.prediction_length:, :] # [B N L K]
-
-    def sample(self, observed_data, cond_mask, side_info, n_samples):
-        B, K, L = observed_data.shape
-        imputed_samples = torch.zeros(B, n_samples, K, L).to(observed_data.device)
-
-        for i in range(n_samples):
-            current_sample = torch.randn_like(observed_data).to(observed_data.device)
-
-            for t in range(self.num_steps - 1, -1, -1):
-                cond_obs = (cond_mask * observed_data).unsqueeze(1)
-                noisy_target = ((1 - cond_mask) * current_sample).unsqueeze(1) # [B 1 K L]
-                diff_input = torch.cat([cond_obs, noisy_target], dim=1)  # (B,2,K,L)
-                predicted = self.diffmodel(diff_input, side_info, torch.tensor([t]).to(observed_data.device))
-
-                coeff1 = 1 / self.alpha_hat[t] ** 0.5
-                coeff2 = (1 - self.alpha_hat[t]) / (1 - self.alpha[t]) ** 0.5
-                current_sample = coeff1 * (current_sample - coeff2 * predicted)
-
-                if t > 0:
-                    noise = torch.randn_like(current_sample).to(observed_data.device)
-                    sigma = (
-                        (1.0 - self.alpha[t - 1]) / (1.0 - self.alpha[t]) * self.beta[t]
-                    ) ** 0.5
-                    current_sample += sigma * noise
-
-            imputed_samples[:, i] = current_sample.detach()
-        return imputed_samples
-
-
diff --git a/probts/model/forecaster/prob_forecaster/gru_maf.py b/probts/model/forecaster/prob_forecaster/gru_maf.py
deleted file mode 100644
index 58ab17e..0000000
--- a/probts/model/forecaster/prob_forecaster/gru_maf.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-
-from probts.data import ProbTSBatchData
-from probts.utils import repeat
-from probts.model.forecaster import Forecaster
-from probts.model.nn import MAF
-
-
-class GRU_MAF(Forecaster):
-    def __init__(
-        self,
-        enc_num_layers: int = 2,
-        enc_hidden_size: int = 40,
-        enc_dropout: float = 0.1,
-        n_blocks: int = 4,
-        hidden_size: int = 100,
-        n_hidden: int = 2,
-        conditional_length: int = 200,
-        dequantize: bool = False,
-        batch_norm: bool = True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.autoregressive = True
-        
-        self.encoder = nn.GRU(
-            input_size=self.input_size,
-            hidden_size=enc_hidden_size,
-            num_layers=enc_num_layers,
-            dropout=enc_dropout,
-            batch_first=True
-        )
-        self.prob_model = MAF(
-            n_blocks=n_blocks,
-            target_dim=self.target_dim,
-            hidden_size=hidden_size,
-            n_hidden=n_hidden,
-            f_hidden_size=enc_hidden_size,
-            conditional_length=conditional_length,
-            dequantize=dequantize,
-            batch_norm=batch_norm
-        )
-
-    def loss(self, batch_data):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-            self.prob_model.scale = self.scaler.scale
-        
-        inputs = self.get_inputs(batch_data, 'all')
-        enc_outs, states = self.encoder(inputs)
-        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
-        
-        dist_args = self.prob_model.dist_args(enc_outs)
-        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples=None):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-        
-        states = self.encode(batch_data)
-        
-        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
-        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
-        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
-        repeated_states = repeat(states, num_samples, dim=1)
-        if self.use_scaling:
-            repeated_scale = repeat(self.scaler.scale, num_samples)
-            self.scaler.scale = repeated_scale
-            self.prob_model.scale = repeated_scale
-
-        future_samples = []
-        for k in range(self.prediction_length):
-            repeated_batch_data = ProbTSBatchData({
-                'target_dimension_indicator': repeated_target_dimension_indicator,
-                'past_target_cdf': repeated_past_target_cdf,
-                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
-            }, device=batch_data.device)
-
-            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
-            # Sample
-            dist_args = self.prob_model.dist_args(enc_outs)
-            new_samples = self.prob_model.sample(cond=dist_args)
-            future_samples.append(new_samples)
-
-            repeated_past_target_cdf = torch.cat(
-                (repeated_past_target_cdf, new_samples), dim=1
-            )
-
-        forecasts = torch.cat(future_samples, dim=1).reshape(
-            -1, num_samples, self.prediction_length, self.target_dim)
-        return forecasts
-
-    def encode(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'encode')
-        outputs, states = self.encoder(inputs)
-        return states
-
-    def decode(self, batch_data, states=None):
-        inputs = self.get_inputs(batch_data, 'decode')
-        outputs, states = self.encoder(inputs, states)
-        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/gru_nvp.py b/probts/model/forecaster/prob_forecaster/gru_nvp.py
deleted file mode 100644
index 1c00f0f..0000000
--- a/probts/model/forecaster/prob_forecaster/gru_nvp.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-
-from probts.data import ProbTSBatchData
-from probts.utils import repeat
-from probts.model.forecaster import Forecaster
-from probts.model.nn import RealNVP
-
-
-class GRU_NVP(Forecaster):
-    def __init__(
-        self,
-        enc_num_layers: int = 2,
-        enc_hidden_size: int = 40,
-        enc_dropout: float = 0.1,
-        n_blocks: int = 4,
-        hidden_size: int = 100,
-        n_hidden: int = 2,
-        conditional_length: int = 200,
-        dequantize: bool = False,
-        batch_norm: bool = True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.autoregressive = True
-        
-        self.encoder = nn.GRU(
-            input_size=self.input_size,
-            hidden_size=enc_hidden_size,
-            num_layers=enc_num_layers,
-            dropout=enc_dropout,
-            batch_first=True
-        )
-        self.prob_model = RealNVP(
-            n_blocks=n_blocks,
-            target_dim=self.target_dim,
-            hidden_size=hidden_size,
-            n_hidden=n_hidden,
-            f_hidden_size=enc_hidden_size,
-            conditional_length=conditional_length,
-            dequantize=dequantize,
-            batch_norm=batch_norm
-        )
-
-    def loss(self, batch_data):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-            self.prob_model.scale = self.scaler.scale
-        
-        inputs = self.get_inputs(batch_data, 'all')
-        enc_outs, states = self.encoder(inputs)
-        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
-        
-        dist_args = self.prob_model.dist_args(enc_outs)
-        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples=None):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-        
-        states = self.encode(batch_data)
-        
-        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
-        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
-        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
-        repeated_states = repeat(states, num_samples, dim=1)
-        if self.use_scaling:
-            repeated_scale = repeat(self.scaler.scale, num_samples)
-            self.scaler.scale = repeated_scale
-            self.prob_model.scale = repeated_scale
-
-        future_samples = []
-        for k in range(self.prediction_length):
-            repeated_batch_data = ProbTSBatchData({
-                'target_dimension_indicator': repeated_target_dimension_indicator,
-                'past_target_cdf': repeated_past_target_cdf,
-                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
-            }, device=batch_data.device)
-
-            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
-            # Sample
-            dist_args = self.prob_model.dist_args(enc_outs)
-            new_samples = self.prob_model.sample(cond=dist_args)
-            future_samples.append(new_samples)
-
-            repeated_past_target_cdf = torch.cat(
-                (repeated_past_target_cdf, new_samples), dim=1
-            )
-
-        forecasts = torch.cat(future_samples, dim=1).reshape(
-            -1, num_samples, self.prediction_length, self.target_dim)
-        return forecasts
-
-    def encode(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'encode')
-        outputs, states = self.encoder(inputs)
-        return states
-
-    def decode(self, batch_data, states=None):
-        inputs = self.get_inputs(batch_data, 'decode')
-        outputs, states = self.encoder(inputs, states)
-        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/lag_llama.py b/probts/model/forecaster/prob_forecaster/lag_llama.py
deleted file mode 100644
index fded772..0000000
--- a/probts/model/forecaster/prob_forecaster/lag_llama.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from lag-llama
-# - Source: https://github.com/time-series-foundation-models/lag-llama
-# - Paper: Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
-# - License: Apache License 2.0
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import numpy as np
-import torch
-
-from gluonts.dataset.common import ListDataset
-
-from probts.model.forecaster import Forecaster
-from submodules.lag_llama.lag_llama.gluon.estimator import LagLlamaEstimator
-
-
-class LagLlama(Forecaster):
-    def __init__(
-        self,
-        use_rope_scaling: bool = True,
-        ckpt_path: str = None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        
-        self.ctx_len = kwargs.get('context_length')
-        self.pred_len = kwargs.get('prediction_length')
-
-        # Load pretrained model
-        self.no_training = True
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        ckpt = torch.load(ckpt_path, map_location=device)
-        estimator_args = ckpt["hyper_parameters"]["model_kwargs"]
-        rope_scaling_arguments = {
-            "type": "linear",
-            "factor": max(1.0, (self.ctx_len + self.pred_len) / estimator_args["context_length"]), # 32
-        }
-        # Load model checkpoint
-        estimator = LagLlamaEstimator(
-            ckpt_path=ckpt_path,
-            prediction_length=self.pred_len,
-            context_length=self.ctx_len, # Lag-Llama was trained with a context length of 32, but can work with any context length
-
-            # estimator args
-            input_size=estimator_args["input_size"], # 1
-            n_layer=estimator_args["n_layer"], # 8
-            n_embd_per_head=estimator_args["n_embd_per_head"], # 16
-            n_head=estimator_args["n_head"], # 9
-            scaling=estimator_args["scaling"], # robust
-            time_feat=estimator_args["time_feat"], # True
-            rope_scaling=rope_scaling_arguments if use_rope_scaling else None, # long-term set to True
-
-            batch_size=4,
-            num_parallel_samples=100,
-            device=device,
-        )
-
-        lightning_module = estimator.create_lightning_module()
-        transformation = estimator.create_transformation()
-        self.predictor = estimator.create_predictor(transformation, lightning_module)
-
-    
-    def forecast(self, batch_data, num_samples=None):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = inputs[:, -self.context_length:]
-        datastamps = batch_data.past_time_feat.cpu().numpy().astype('datetime64[s]')
-
-        # for now, we only support batch_size=1
-        B, _, K = inputs.shape 
-        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
-        start_time = datastamps.reshape(-1)[0]
-        data = [{"start": start_time, "target": inputs[:,:,i].cpu().squeeze()} for i in range(K)]
-        dataset = ListDataset(data, freq='1h')
-
-        forecasts = self.predictor.predict(dataset, num_samples=num_samples)
-        samples = [fs.samples for fs in forecasts]
-        forecasts = np.array(samples).transpose(1, 2, 0)
-
-        prob_forecast = forecasts[np.newaxis, :, :]
-        prob_forecast = torch.tensor(prob_forecast) # shape: b s l k
-        
-        return prob_forecast
diff --git a/probts/model/forecaster/prob_forecaster/moirai.py b/probts/model/forecaster/prob_forecaster/moirai.py
deleted file mode 100644
index 5219d0a..0000000
--- a/probts/model/forecaster/prob_forecaster/moirai.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from uni2ts
-# - Source: https://github.com/SalesforceAIResearch/uni2ts
-# - Paper: Unified Training of Universal Time Series Forecasting Transformers
-# - License: Apache License 2.0
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-from typing import Union
-from probts.model.forecaster import Forecaster
-from einops import rearrange, repeat 
-from probts.model.nn.layers.Moirai_backbone import MoiraiBackbone
-from uni2ts.model.moirai.module import MoiraiModule
-
-
-class Moirai(Forecaster):
-    def __init__(
-        self,
-        variate_mode: str = 'M',
-        patch_size: Union[str, int] = 'auto',
-        model_size: str = 'base',
-        scaling: bool = True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.variate_mode = variate_mode
-        self.patch_size = patch_size if patch_size == 'auto' else int(patch_size)
-        
-        # Load pretrained model
-        self.no_training = True
-        self.moirai = MoiraiBackbone(
-            module=MoiraiModule.from_pretrained(f"Salesforce/moirai-1.0-R-{model_size}"),
-            prediction_length=self.prediction_length,
-            context_length=self.context_length,
-            patch_size=self.patch_size,
-            target_dim=self.target_dim if self.variate_mode == 'M' else 1,
-            scaling=scaling
-        )
-
-    def forecast(self, batch_data, num_samples=None):
-        if self.variate_mode == 'M':
-            forecasts = self.moirai(
-                past_target=batch_data.past_target_cdf,
-                past_observed_target=batch_data.past_observed_values,
-                past_is_pad=batch_data.past_is_pad,
-                num_samples=num_samples
-            )
-        elif self.variate_mode == 'S':
-            B, L, K = batch_data.past_target_cdf.shape
-            forecasts = self.moirai(
-                past_target=rearrange(batch_data.past_target_cdf, 'b l k -> (b k) l').unsqueeze(-1),
-                past_observed_target=rearrange(batch_data.past_observed_values, 'b l k -> (b k) l').unsqueeze(-1),
-                past_is_pad=repeat(batch_data.past_is_pad, 'b l -> (b k) l', k=K),
-                num_samples=num_samples
-            )
-            forecasts = forecasts.squeeze(-1)
-            forecasts = rearrange(forecasts, '(b k) n l -> b n l k', b=B, k=K)
-        else:
-            raise ValueError(f"Unknown variate mode: {self.variate_mode}")
-        return forecasts
diff --git a/probts/model/forecaster/prob_forecaster/timegrad.py b/probts/model/forecaster/prob_forecaster/timegrad.py
deleted file mode 100644
index a0cd8ea..0000000
--- a/probts/model/forecaster/prob_forecaster/timegrad.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-
-from probts.data import ProbTSBatchData
-from probts.utils import repeat
-from probts.model.forecaster import Forecaster
-from probts.model.nn import GaussianDiffusion
-
-
-class TimeGrad(Forecaster):
-    def __init__(
-        self,
-        enc_num_layers: int = 2,
-        enc_hidden_size: int = 40,
-        enc_dropout: float = 0.1,
-        conditional_length: int = 100,
-        beta_end: float = 0.1,
-        diff_steps: int = 100,
-        loss_type: str = "l2",
-        beta_schedule: str = "linear",
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.autoregressive = True
-        
-        self.encoder = nn.GRU(
-            input_size=self.input_size,
-            hidden_size=enc_hidden_size,
-            num_layers=enc_num_layers,
-            dropout=enc_dropout,
-            batch_first=True
-        )
-        self.prob_model = GaussianDiffusion(
-            target_dim=self.target_dim,
-            f_hidden_size=enc_hidden_size,
-            conditional_length=conditional_length,
-            beta_end=beta_end,
-            diff_steps=diff_steps,
-            loss_type=loss_type,
-            beta_schedule=beta_schedule
-        )
-
-    def loss(self, batch_data):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-            self.prob_model.scale = self.scaler.scale
-        
-        inputs = self.get_inputs(batch_data, 'all')
-        enc_outs, states = self.encoder(inputs)
-        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
-        
-        dist_args = self.prob_model.dist_args(enc_outs)
-        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples=None):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-        
-        states = self.encode(batch_data)
-        
-        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
-        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
-        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
-        repeated_states = repeat(states, num_samples, dim=1)
-        if self.use_scaling:
-            repeated_scale = repeat(self.scaler.scale, num_samples)
-            self.scaler.scale = repeated_scale
-            self.prob_model.scale = repeated_scale
-
-        future_samples = []
-        for k in range(self.prediction_length):
-            repeated_batch_data = ProbTSBatchData({
-                'target_dimension_indicator': repeated_target_dimension_indicator,
-                'past_target_cdf': repeated_past_target_cdf,
-                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
-            }, device=batch_data.device)
-
-            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
-            # Sample
-            dist_args = self.prob_model.dist_args(enc_outs)
-            new_samples = self.prob_model.sample(cond=dist_args)
-            future_samples.append(new_samples)
-
-            repeated_past_target_cdf = torch.cat(
-                (repeated_past_target_cdf, new_samples), dim=1
-            )
-
-        forecasts = torch.cat(future_samples, dim=1).reshape(
-            -1, num_samples, self.prediction_length, self.target_dim)
-        return forecasts
-
-    def encode(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'encode')
-        outputs, states = self.encoder(inputs)
-        return states
-
-    def decode(self, batch_data, states=None):
-        inputs = self.get_inputs(batch_data, 'decode')
-        outputs, states = self.encoder(inputs, states)
-        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/trans_maf.py b/probts/model/forecaster/prob_forecaster/trans_maf.py
deleted file mode 100644
index 662175b..0000000
--- a/probts/model/forecaster/prob_forecaster/trans_maf.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-
-from probts.data import ProbTSBatchData
-from probts.utils import repeat
-from probts.model.forecaster import Forecaster
-from probts.model.nn import MAF
-
-
-class Trans_MAF(Forecaster):
-    def __init__(
-        self,
-        enc_hidden_size: int = 32,
-        enc_num_heads: int = 8,
-        enc_num_encoder_layers: int = 3,
-        enc_num_decoder_layers: int = 3,
-        enc_dim_feedforward_scale: int = 4,
-        enc_dropout: float = 0.1,
-        enc_activation: str = 'gelu',
-        n_blocks: int = 4,
-        hidden_size: int = 100,
-        n_hidden: int = 2,
-        conditional_length: int = 200,
-        dequantize: bool = False,
-        batch_norm: bool = True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.autoregressive = True
-
-        self.enc_linear = nn.Linear(self.input_size, enc_hidden_size)
-        self.dec_linear = nn.Linear(self.input_size, enc_hidden_size)
-        self.model = nn.Transformer(
-            d_model=enc_hidden_size,
-            nhead=enc_num_heads,
-            num_encoder_layers=enc_num_encoder_layers,
-            num_decoder_layers=enc_num_decoder_layers,
-            dim_feedforward=enc_dim_feedforward_scale * enc_hidden_size,
-            dropout=enc_dropout,
-            activation=enc_activation
-        )
-
-        self.register_buffer(
-            "tgt_mask",
-            self.model.generate_square_subsequent_mask(self.prediction_length),
-        )
-        
-        self.prob_model = MAF(
-            n_blocks=n_blocks,
-            target_dim=self.target_dim,
-            hidden_size=hidden_size,
-            n_hidden=n_hidden,
-            f_hidden_size=enc_hidden_size,
-            conditional_length=conditional_length,
-            dequantize=dequantize,
-            batch_norm=batch_norm
-        )
-
-    def loss(self, batch_data):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-            self.prob_model.scale = self.scaler.scale
-        
-        inputs = self.get_inputs(batch_data, 'all') # [B L D]
-
-        enc_inputs = inputs[:, :self.context_length, ...]
-        enc_inputs = self.enc_linear(enc_inputs).permute(1, 0, 2)
-        enc_outputs = self.model.encoder(enc_inputs) # [L_in B H]
-
-        dec_inputs = inputs[:, -self.prediction_length-1:-1, ...]
-        dec_inputs = self.dec_linear(dec_inputs).permute(1, 0, 2)
-        dec_outputs = self.model.decoder(
-            dec_inputs, enc_outputs, tgt_mask=self.tgt_mask)
-        dec_outputs = dec_outputs.permute(1, 0, 2)  # [L_out B D]
-        
-        dist_args = self.prob_model.dist_args(dec_outputs)
-        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
-        loss = self.get_weighted_loss(batch_data, loss)
-        return loss.mean()
-
-    def forecast(self, batch_data, num_samples=None):
-        if self.use_scaling:
-            self.get_scale(batch_data)
-        
-        states = self.encode(batch_data)
-        
-        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
-        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
-        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
-        repeated_states = repeat(states, num_samples, dim=1)
-        if self.use_scaling:
-            repeated_scale = repeat(self.scaler.scale, num_samples)
-            self.scaler.scale = repeated_scale
-            self.prob_model.scale = repeated_scale
-
-        future_samples = []
-        for k in range(self.prediction_length):
-            repeated_batch_data = ProbTSBatchData({
-                'target_dimension_indicator': repeated_target_dimension_indicator,
-                'past_target_cdf': repeated_past_target_cdf,
-                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
-            }, device=batch_data.device)
-
-            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
-            # Sample
-            dist_args = self.prob_model.dist_args(enc_outs)
-            new_samples = self.prob_model.sample(cond=dist_args)
-            future_samples.append(new_samples)
-
-            repeated_past_target_cdf = torch.cat(
-                (repeated_past_target_cdf, new_samples), dim=1
-            )
-
-        forecasts = torch.cat(future_samples, dim=1).reshape(
-            -1, num_samples, self.prediction_length, self.target_dim)
-        return forecasts
-
-    def encode(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'encode')
-        inputs = self.enc_linear(inputs).permute(1, 0, 2)
-        states = self.model.encoder(inputs)
-        return states
-
-    def decode(self, batch_data, states=None):
-        inputs = self.get_inputs(batch_data, 'decode')
-        inputs = self.dec_linear(inputs).permute(1, 0, 2)
-        outputs = self.model.decoder(inputs, states, tgt_mask=None)
-        return outputs.permute(1, 0, 2), states
diff --git a/probts/model/forecaster/prob_forecaster/tsdiff.py b/probts/model/forecaster/prob_forecaster/tsdiff.py
deleted file mode 100644
index 3a22172..0000000
--- a/probts/model/forecaster/prob_forecaster/tsdiff.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from TSDiff
-# - Source: https://github.com/amazon-science/unconditional-time-series-diffusion
-# - Paper: Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
-# - License: Apache-2.0
-#
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-import torch
-import torch.nn as nn
-from probts.model.nn.layers.s4_backbones import BackboneModel
-from einops import rearrange
-from probts.utils import repeat
-import torch.nn.functional as F
-from probts.utils import extract
-from probts.model.forecaster import Forecaster
-import sys
-import numpy as np
-
-def linear_beta_schedule(timesteps):
-    beta_start = 0.0001
-    beta_end = 0.1
-    return torch.linspace(beta_start, beta_end, timesteps)
-
-
-class TSDiffCond(Forecaster):
-    def __init__(
-        self,
-        hidden_dim: int,
-        step_emb: int,
-        timesteps: int,
-        num_residual_blocks: int,
-        dropout: float = 0,
-        # use_features: bool = False,
-        init_skip=True,
-        noise_observed=False, # reconstruct past
-        mode="diag",
-        measure="diag",
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        backbone_parameters = {
-            "input_dim": self.target_dim,
-            "hidden_dim": hidden_dim,
-            "output_dim": self.target_dim,
-            "step_emb": step_emb,
-            "num_residual_blocks": num_residual_blocks,
-            "residual_block": "s4",
-            "mode": mode,
-            'measure': measure,
-        }
-        # self.use_features=use_features
-        self.timesteps = timesteps
-        self.betas = linear_beta_schedule(timesteps)
-        self.sqrt_one_minus_beta = torch.sqrt(1.0 - self.betas)
-        self.alphas = 1 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, axis=0)
-        self.alphas_cumprod_prev = F.pad(
-            self.alphas_cumprod[:-1], (1, 0), value=1.0
-        )
-        self.sqrt_recip_alphas = torch.sqrt(1.0 / self.alphas)
-        self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(
-            1.0 - self.alphas_cumprod
-        )
-        self.posterior_variance = (
-            self.betas
-            * (1.0 - self.alphas_cumprod_prev)
-            / (1.0 - self.alphas_cumprod)
-        )
-        self.backbone = BackboneModel(
-            **backbone_parameters,
-            num_features=self.target_dim,
-            init_skip=init_skip,
-            dropout=dropout,
-        )
-        self.noise_observed = noise_observed
-
-    def _extract_features(self, batch_data):
-        inputs = self.get_inputs(batch_data, 'all')
-        x = inputs[:,:, :self.target_dim]
-        features = inputs.clone()
-        
-        if self.use_time_feat:
-            features[:,self.context_length:, :self.target_dim] = 0
-        else:
-            features = features[:,:, :self.target_dim]
-            features[:,self.context_length:] = 0
-        
-        observation_mask = torch.zeros_like(x, device=x.device)
-        observation_mask[:,:self.context_length] = 1
-        
-        return x, features, observation_mask
-
-    def q_sample(self, x_start, t, noise=None):
-        device = next(self.backbone.parameters()).device
-        if noise is None:
-            noise = torch.randn_like(x_start, device=device)
-        sqrt_alphas_cumprod_t = extract(
-            self.sqrt_alphas_cumprod, t, x_start.shape
-        )
-        sqrt_one_minus_alphas_cumprod_t = extract(
-            self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
-        )
-
-        return (
-            sqrt_alphas_cumprod_t * x_start
-            + sqrt_one_minus_alphas_cumprod_t * noise
-        )
-
-    def p_losses(
-        self,
-        x_start,
-        t,
-        features=None,
-        noise=None,
-        loss_type="l2",
-        reduction="none",
-    ):
-        device = next(self.backbone.parameters()).device
-        if noise is None:
-            noise = torch.randn_like(x_start, device=device)
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        predicted_noise = self.backbone(x_noisy, t, features)
-
-        if loss_type == "l1":
-            loss = F.l1_loss(noise, predicted_noise, reduction=reduction)
-        elif loss_type == "l2":
-            loss = F.mse_loss(noise, predicted_noise, reduction=reduction)
-        elif loss_type == "huber":
-            loss = F.smooth_l1_loss(
-                noise, predicted_noise, reduction=reduction
-            )
-        else:
-            raise NotImplementedError()
-
-        return loss, x_noisy, predicted_noise
-
-    @torch.no_grad()
-    def p_sample(self, x, t, t_index, features=None):
-        betas_t = extract(self.betas, t, x.shape)
-        sqrt_one_minus_alphas_cumprod_t = extract(
-            self.sqrt_one_minus_alphas_cumprod, t, x.shape
-        )
-        sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)
-
-
-        predicted_noise = self.backbone(x, t, features)
-
-        model_mean = sqrt_recip_alphas_t * (
-            x - betas_t * predicted_noise / sqrt_one_minus_alphas_cumprod_t
-        )
-
-        if t_index == 0:
-            return model_mean
-        else:
-            posterior_variance_t = extract(self.posterior_variance, t, x.shape)
-            noise = torch.randn_like(x)
-            return model_mean + torch.sqrt(posterior_variance_t) * noise
-
-    def step(self, x, t, features, loss_mask):
-        noise = torch.randn_like(x)
-        if not self.noise_observed:
-            noise = (1 - loss_mask) * x + noise * loss_mask
-
-        num_eval = loss_mask.sum()
-        sq_err, _, _ = self.p_losses(
-            x,
-            t,
-            features,
-            loss_type="l2",
-            reduction="none",
-            noise=noise,
-        )
-
-        if self.noise_observed:
-            elbo_loss = sq_err.mean()
-        else:
-            sq_err = sq_err * loss_mask
-            elbo_loss = sq_err.sum() / (num_eval if num_eval else 1)
-        return elbo_loss
-
-
-
-    def loss(self, batch_data):
-        # [b l k 1], [b l k 2]
-        x, features, observation_mask = self._extract_features(batch_data)
-        loss_mask = 1 - observation_mask
-
-        t = torch.randint(
-            0, self.timesteps, [x.shape[0]], device=x.device
-        ).long()
-        
-        loss = self.step(x, t, features, loss_mask)
-
-        if torch.isnan(loss):
-            print("Loss is NaN, exiting.")
-            sys.exit(1)
-        return loss
-
-    def forecast(self, batch_data, num_samples):
-        observation, features, observation_mask = self._extract_features(batch_data)
-
-        observation = observation.to(observation.device)
-
-        pred = self.sample(
-            observation=observation,
-            observation_mask=observation_mask,
-            n_samples=num_samples,
-            features=features,
-        )  
-
-        return pred[:,:,-self.prediction_length:,:]
-
-    @torch.no_grad()
-    def sample(self, observation, observation_mask, n_samples, features=None):
-
-        repeated_observation = repeat(observation, n_samples)
-        repeated_observation_mask = repeat(observation_mask, n_samples)
-        repeated_features = repeat(features, n_samples)
-        
-        batch_size, length, ch = repeated_observation.shape
-        seq = torch.randn_like(repeated_observation)
-
-        for i in reversed(range(0, self.timesteps)):
-            if not self.noise_observed:
-                seq = repeated_observation_mask * repeated_observation + seq * (1 - repeated_observation_mask)
-
-            seq = self.p_sample(
-                seq,
-                torch.full((batch_size,), i, device=repeated_observation.device, dtype=torch.long),
-                i,
-                repeated_features,
-            )
-
-        seq = seq.reshape(-1, n_samples, length, ch)
-        return seq 
diff --git a/probts/model/nn/MAF.py b/probts/model/nn/MAF.py
deleted file mode 100644
index b767605..0000000
--- a/probts/model/nn/MAF.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.distributions import Normal
-from .flow_model import FlowModel, BatchNorm, FlowSequential
-
-
-def create_masks(
-    input_size, hidden_size, n_hidden, input_order="sequential", input_degrees=None
-):
-    # MADE paper sec 4:
-    # degrees of connections between layers -- ensure at most in_degree - 1 connections
-    degrees = []
-
-    # set input degrees to what is provided in args (the flipped order of the previous layer in a stack of mades);
-    # else init input degrees based on strategy in input_order (sequential or random)
-    if input_order == "sequential":
-        degrees += (
-            [torch.arange(input_size)] if input_degrees is None else [input_degrees]
-        )
-        for _ in range(n_hidden + 1):
-            degrees += [torch.arange(hidden_size) % (input_size - 1)]
-        degrees += (
-            [torch.arange(input_size) % input_size - 1]
-            if input_degrees is None
-            else [input_degrees % input_size - 1]
-        )
-
-    elif input_order == "random":
-        degrees += (
-            [torch.randperm(input_size)] if input_degrees is None else [input_degrees]
-        )
-        for _ in range(n_hidden + 1):
-            min_prev_degree = min(degrees[-1].min().item(), input_size - 1)
-            degrees += [torch.randint(min_prev_degree, input_size, (hidden_size,))]
-        min_prev_degree = min(degrees[-1].min().item(), input_size - 1)
-        degrees += (
-            [torch.randint(min_prev_degree, input_size, (input_size,)) - 1]
-            if input_degrees is None
-            else [input_degrees - 1]
-        )
-
-    # construct masks
-    masks = []
-    for (d0, d1) in zip(degrees[:-1], degrees[1:]):
-        masks += [(d1.unsqueeze(-1) >= d0.unsqueeze(0)).float()]
-
-    return masks, degrees[0]
-
-
-class MaskedLinear(nn.Linear):
-    """ MADE building block layer """
-
-    def __init__(self, input_size, n_outputs, mask, cond_label_size=None):
-        super().__init__(input_size, n_outputs)
-
-        self.register_buffer("mask", mask)
-
-        self.cond_label_size = cond_label_size
-        if cond_label_size is not None:
-            self.cond_weight = nn.Parameter(
-                torch.rand(n_outputs, cond_label_size) / math.sqrt(cond_label_size)
-            )
-
-    def forward(self, x, y=None):
-        out = F.linear(x, self.weight * self.mask, self.bias)
-        if y is not None:
-            out = out + F.linear(y, self.cond_weight)
-        return out
-
-
-class MADE(nn.Module):
-    def __init__(
-        self,
-        input_size,
-        hidden_size,
-        n_hidden,
-        cond_label_size=None,
-        activation="ReLU",
-        input_order="sequential",
-        input_degrees=None,
-    ):
-        """
-        Args:
-            input_size -- scalar; dim of inputs
-            hidden_size -- scalar; dim of hidden layers
-            n_hidden -- scalar; number of hidden layers
-            activation -- str; activation function to use
-            input_order -- str or tensor; variable order for creating the autoregressive masks (sequential|random)
-                            or the order flipped from the previous layer in a stack of MADEs
-            conditional -- bool; whether model is conditional
-        """
-        super().__init__()
-        # base distribution for calculation of log prob under the model
-        self.register_buffer("base_dist_mean", torch.zeros(input_size))
-        self.register_buffer("base_dist_var", torch.ones(input_size))
-
-        # create masks
-        masks, self.input_degrees = create_masks(
-            input_size, hidden_size, n_hidden, input_order, input_degrees
-        )
-
-        # setup activation
-        if activation == "ReLU":
-            activation_fn = nn.ReLU()
-        elif activation == "Tanh":
-            activation_fn = nn.Tanh()
-        else:
-            raise ValueError("Check activation function.")
-
-        # construct model
-        self.net_input = MaskedLinear(
-            input_size, hidden_size, masks[0], cond_label_size
-        )
-        self.net = []
-        for m in masks[1:-1]:
-            self.net += [activation_fn, MaskedLinear(hidden_size, hidden_size, m)]
-        self.net += [
-            activation_fn,
-            MaskedLinear(hidden_size, 2 * input_size, masks[-1].repeat(2, 1)),
-        ]
-        self.net = nn.Sequential(*self.net)
-
-    @property
-    def base_dist(self):
-        return Normal(self.base_dist_mean, self.base_dist_var)
-
-    def forward(self, x, y=None):
-        # MAF eq 4 -- return mean and log std
-        m, loga = self.net(self.net_input(x, y)).chunk(chunks=2, dim=-1)
-        u = (x - m) * torch.exp(-loga)
-        # MAF eq 5
-        log_abs_det_jacobian = -loga
-        return u, log_abs_det_jacobian
-
-    def inverse(self, u, y=None, sum_log_abs_det_jacobians=None):
-        # MAF eq 3
-        # D = u.shape[-1]
-        x = torch.zeros_like(u)
-        # run through reverse model
-        for i in self.input_degrees:
-            m, loga = self.net(self.net_input(x, y)).chunk(chunks=2, dim=-1)
-            x[..., i] = u[..., i] * torch.exp(loga[..., i]) + m[..., i]
-        log_abs_det_jacobian = loga
-        return x, log_abs_det_jacobian
-
-    def log_prob(self, x, y=None):
-        u, log_abs_det_jacobian = self.forward(x, y)
-        return torch.sum(self.base_dist.log_prob(u) + log_abs_det_jacobian, dim=-1)
-
-
-class MAF(FlowModel):
-    def __init__(
-        self,
-        n_blocks,
-        target_dim,
-        hidden_size,
-        n_hidden,
-        f_hidden_size,
-        conditional_length,
-        dequantize,
-        activation="ReLU",
-        input_order="sequential",
-        batch_norm=True,
-    ):
-        super().__init__(target_dim, f_hidden_size, conditional_length, dequantize)
-
-        # construct model
-        modules = []
-        self.input_degrees = None
-        for i in range(n_blocks):
-            modules += [
-                MADE(
-                    target_dim,
-                    hidden_size,
-                    n_hidden,
-                    conditional_length,
-                    activation,
-                    input_order,
-                    self.input_degrees,
-                )
-            ]
-            self.input_degrees = modules[-1].input_degrees.flip(0)
-            modules += batch_norm * [BatchNorm(target_dim)]
-
-        self.net = FlowSequential(*modules)
\ No newline at end of file
diff --git a/probts/model/nn/RealNVP.py b/probts/model/nn/RealNVP.py
deleted file mode 100644
index 82c7f17..0000000
--- a/probts/model/nn/RealNVP.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import copy
-import torch
-import torch.nn as nn
-from .flow_model import FlowModel, BatchNorm, FlowSequential
-
-
-class LinearMaskedCoupling(nn.Module):
-    """ Modified RealNVP Coupling Layers per the MAF paper """
-
-    def __init__(self, input_size, hidden_size, n_hidden, mask, cond_label_size=None):
-        super().__init__()
-
-        self.register_buffer("mask", mask)
-
-        # scale function
-        s_net = [
-            nn.Linear(
-                input_size + (cond_label_size if cond_label_size is not None else 0),
-                hidden_size,
-            )
-        ]
-        for _ in range(n_hidden):
-            s_net += [nn.Tanh(), nn.Linear(hidden_size, hidden_size)]
-        s_net += [nn.Tanh(), nn.Linear(hidden_size, input_size)]
-        self.s_net = nn.Sequential(*s_net)
-
-        # translation function
-        self.t_net = copy.deepcopy(self.s_net)
-        # replace Tanh with ReLU's per MAF paper
-        for i in range(len(self.t_net)):
-            if not isinstance(self.t_net[i], nn.Linear):
-                self.t_net[i] = nn.ReLU()
-
-    def forward(self, x, y=None):
-        # apply mask
-        mx = x * self.mask
-
-        # run through model
-        s = self.s_net(mx if y is None else torch.cat([y, mx], dim=-1))
-        t = self.t_net(mx if y is None else torch.cat([y, mx], dim=-1)) * (
-            1 - self.mask
-        )
-
-        # cf RealNVP eq 8 where u corresponds to x (here we're modeling u)
-        log_s = torch.tanh(s) * (1 - self.mask)
-        u = x * torch.exp(log_s) + t
-        # u = (x - t) * torch.exp(log_s)
-        # u = mx + (1 - self.mask) * (x - t) * torch.exp(-s)
-
-        # log det du/dx; cf RealNVP 8 and 6; note, sum over input_size done at model log_prob
-        # log_abs_det_jacobian = -(1 - self.mask) * s
-        # log_abs_det_jacobian = -log_s #.sum(-1, keepdim=True)
-        log_abs_det_jacobian = log_s
-
-        return u, log_abs_det_jacobian
-
-    def inverse(self, u, y=None):
-        # apply mask
-        mu = u * self.mask
-
-        # run through model
-        s = self.s_net(mu if y is None else torch.cat([y, mu], dim=-1))
-        t = self.t_net(mu if y is None else torch.cat([y, mu], dim=-1)) * (
-            1 - self.mask
-        )
-
-        log_s = torch.tanh(s) * (1 - self.mask)
-        x = (u - t) * torch.exp(-log_s)
-        # x = u * torch.exp(log_s) + t
-        # x = mu + (1 - self.mask) * (u * s.exp() + t)  # cf RealNVP eq 7
-
-        # log_abs_det_jacobian = (1 - self.mask) * s  # log det dx/du
-        # log_abs_det_jacobian = log_s #.sum(-1, keepdim=True)
-        log_abs_det_jacobian = -log_s
-
-        return x, log_abs_det_jacobian
-
-
-class RealNVP(FlowModel):
-    def __init__(
-        self,
-        n_blocks,
-        target_dim,
-        hidden_size,
-        n_hidden,
-        f_hidden_size,
-        conditional_length,
-        dequantize,
-        batch_norm=True
-    ):
-        super().__init__(target_dim, f_hidden_size, conditional_length, dequantize)
-
-        # construct model
-        modules = []
-        mask = torch.arange(target_dim).float() % 2
-        for i in range(n_blocks):
-            modules += [
-                LinearMaskedCoupling(
-                    target_dim, hidden_size, n_hidden, mask, conditional_length
-                )
-            ]
-            mask = 1 - mask
-            modules += batch_norm * [BatchNorm(target_dim)]
-
-        self.net = FlowSequential(*modules)
\ No newline at end of file
diff --git a/probts/model/nn/__init__.py b/probts/model/nn/__init__.py
index 5b90d06..e69de29 100644
--- a/probts/model/nn/__init__.py
+++ b/probts/model/nn/__init__.py
@@ -1,3 +0,0 @@
-from .RealNVP import RealNVP
-from .MAF import MAF
-from .gaussian_diffusion import GaussianDiffusion
\ No newline at end of file
diff --git a/probts/model/nn/flow_model.py b/probts/model/nn/flow_model.py
deleted file mode 100644
index 6469a26..0000000
--- a/probts/model/nn/flow_model.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-from torch.distributions import Normal
-
-class FlowModel(nn.Module):
-    def __init__(self, target_dim, f_hidden_size, conditional_length, dequantize):
-        super().__init__()
-        self.__scale = None
-        self.net = None
-        self.dequantize = dequantize
-
-        self.dist_args = nn.Linear(
-            in_features=f_hidden_size, out_features=conditional_length
-        )
-
-        # base distribution for calculation of log prob under the model
-        self.register_buffer("base_dist_mean", torch.zeros(target_dim))
-        self.register_buffer("base_dist_var", torch.ones(target_dim))
-
-    @property
-    def base_dist(self):
-        return Normal(self.base_dist_mean, self.base_dist_var)
-
-    @property
-    def scale(self):
-        return self.__scale
-    
-    @scale.setter
-    def scale(self, scale):
-        self.__scale = scale
-
-    def forward(self, x, cond):
-        if self.scale is not None:
-            x /= self.scale
-        u, log_abs_det_jacobian = self.net(x, cond)
-        return u, log_abs_det_jacobian
-
-    def inverse(self, u, cond):
-        x, log_abs_det_jacobian = self.net.inverse(u, cond)
-        if self.scale is not None:
-            x *= self.scale
-            log_abs_det_jacobian += torch.log(torch.abs(self.scale))
-        return x, log_abs_det_jacobian
-
-    def log_prob(self, x, cond):
-        if self.dequantize:
-            x += torch.rand_like(x)
-        u, sum_log_abs_det_jacobians = self.forward(x, cond)
-        return torch.sum(self.base_dist.log_prob(u) + sum_log_abs_det_jacobians, dim=-1)
-
-    def loss(self, x, cond):
-        return -self.log_prob(x, cond)
-
-    def sample(self, sample_shape=torch.Size(), cond=None):
-        if cond is not None:
-            shape = cond.shape[:-1]
-        else:
-            shape = sample_shape
-
-        u = self.base_dist.sample(shape)
-        sample, _ = self.inverse(u, cond)
-        return sample
-
-
-class BatchNorm(nn.Module):
-    """ Flow Model BatchNorm layer """
-
-    def __init__(self, input_size, momentum=0.9, eps=1e-5):
-        super().__init__()
-        self.momentum = momentum
-        self.eps = eps
-
-        self.log_gamma = nn.Parameter(torch.zeros(input_size))
-        self.beta = nn.Parameter(torch.zeros(input_size))
-
-        self.register_buffer("running_mean", torch.zeros(input_size))
-        self.register_buffer("running_var", torch.ones(input_size))
-
-    def forward(self, x, cond_y=None):
-        if self.training:
-            self.batch_mean = x.view(-1, x.shape[-1]).mean(0)
-            # note MAF paper uses biased variance estimate; ie x.var(0, unbiased=False)
-            self.batch_var = x.view(-1, x.shape[-1]).var(0)
-
-            # update running mean
-            self.running_mean.mul_(self.momentum).add_(
-                self.batch_mean.data * (1 - self.momentum)
-            )
-            self.running_var.mul_(self.momentum).add_(
-                self.batch_var.data * (1 - self.momentum)
-            )
-
-            mean = self.batch_mean
-            var = self.batch_var
-        else:
-            mean = self.running_mean
-            var = self.running_var
-
-        # compute normalized input (cf original batch norm paper algo 1)
-        x_hat = (x - mean) / torch.sqrt(var + self.eps)
-        y = self.log_gamma.exp() * x_hat + self.beta
-
-        # compute log_abs_det_jacobian (cf RealNVP paper)
-        log_abs_det_jacobian = self.log_gamma - 0.5 * torch.log(var + self.eps)
-        
-        return y, log_abs_det_jacobian.expand_as(x)
-
-    def inverse(self, y, cond_y=None):
-        if self.training:
-            mean = self.batch_mean
-            var = self.batch_var
-        else:
-            mean = self.running_mean
-            var = self.running_var
-
-        x_hat = (y - self.beta) * torch.exp(-self.log_gamma)
-        x = x_hat * torch.sqrt(var + self.eps) + mean
-
-        log_abs_det_jacobian = 0.5 * torch.log(var + self.eps) - self.log_gamma
-
-        return x, log_abs_det_jacobian.expand_as(x)
-
-
-class FlowSequential(nn.Sequential):
-    """ Container for layers of a normalizing flow """
-
-    def forward(self, x, y):
-        sum_log_abs_det_jacobians = 0
-        for module in self:
-            x, log_abs_det_jacobian = module(x, y)
-            sum_log_abs_det_jacobians += log_abs_det_jacobian
-        return x, sum_log_abs_det_jacobians
-
-    def inverse(self, u, y):
-        sum_log_abs_det_jacobians = 0
-        for module in reversed(self):
-            u, log_abs_det_jacobian = module.inverse(u, y)
-            sum_log_abs_det_jacobians += log_abs_det_jacobian
-        return u, sum_log_abs_det_jacobians
\ No newline at end of file
diff --git a/probts/model/nn/gaussian_diffusion.py b/probts/model/nn/gaussian_diffusion.py
deleted file mode 100644
index e5f377f..0000000
--- a/probts/model/nn/gaussian_diffusion.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import math
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from torch.distributions import Normal
-from probts.model.forecaster import Forecaster
-from .layers import DiffusionEmbedding
-from functools import partial
-from inspect import isfunction
-
-
-def default(val, d):
-    if val is not None:
-        return val
-    return d() if isfunction(d) else d
-
-
-def extract(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
-        shape[0], *((1,) * (len(shape) - 1))
-    )
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
-
-def cosine_beta_schedule(timesteps, s=0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, timesteps, steps)
-    alphas_cumprod = np.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return np.clip(betas, 0, 0.999)
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, hidden_size, residual_channels, dilation, target_dim):
-        super().__init__()
-        self.target_dim = target_dim
-        
-        self.diffusion_projection = nn.Linear(hidden_size, residual_channels)
-
-        if self.target_dim > 1:
-            self.dilated_conv = nn.Conv1d(
-                residual_channels,
-                2 * residual_channels,
-                3,
-                padding=dilation,
-                dilation=dilation,
-                padding_mode="circular",
-            )
-            self.conditioner_projection = nn.Conv1d(
-                1, 2 * residual_channels, 1, padding=2, padding_mode="circular"
-            )
-        else:
-            self.dilated_conv = nn.Conv1d(residual_channels,2 * residual_channels,1)
-            self.conditioner_projection = nn.Conv1d(1, 2 * residual_channels, 1)
-
-        self.output_projection = nn.Conv1d(residual_channels, 2 * residual_channels, 1)
-
-        nn.init.kaiming_normal_(self.conditioner_projection.weight)
-        nn.init.kaiming_normal_(self.output_projection.weight)
-
-    def forward(self, x, conditioner, diffusion_step):
-        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
-        conditioner = self.conditioner_projection(conditioner)
-
-        y = x + diffusion_step
-        y = self.dilated_conv(y) + conditioner
-
-        gate, filter = torch.chunk(y, 2, dim=1)
-        y = torch.sigmoid(gate) * torch.tanh(filter)
-
-        y = self.output_projection(y)
-        y = F.leaky_relu(y, 0.4)
-        residual, skip = torch.chunk(y, 2, dim=1)
-        return (x + residual) / math.sqrt(2.0), skip
-
-
-class CondUpsampler(nn.Module):
-    def __init__(self, cond_length, target_dim):
-        super().__init__()
-        self.target_dim = target_dim
-
-        if self.target_dim > 1:
-            self.linear1 = nn.Linear(cond_length, target_dim // 2)
-            self.linear2 = nn.Linear(target_dim // 2, target_dim)
-        else:
-            self.linear = nn.Linear(cond_length, target_dim)
-
-    def forward(self, x):
-        if self.target_dim > 1:
-            x = self.linear1(x)
-            x = F.leaky_relu(x, 0.4)
-            x = self.linear2(x)
-            x = F.leaky_relu(x, 0.4)
-        else:
-            x = self.linear(x)
-            x = F.leaky_relu(x, 0.4)
-        return x
-
-
-class EpsilonTheta(nn.Module):
-    def __init__(
-        self,
-        target_dim,
-        cond_length,
-        time_emb_dim=16,
-        residual_layers=8,
-        residual_channels=8,
-        dilation_cycle_length=2,
-        residual_hidden=64,
-        padding=2
-    ):
-        super().__init__()
-        if target_dim > 1:
-            self.input_projection = nn.Conv1d(
-                1, residual_channels, 1, padding=padding, padding_mode="circular"
-            )
-            self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 3)
-            self.output_projection = nn.Conv1d(residual_channels, 1, 3)
-        else:
-            # self.input_projection = nn.Identity()
-            self.input_projection = nn.Conv1d(1, residual_channels, 1)
-            self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 1)
-            self.output_projection = nn.Conv1d(residual_channels, 1, 1)
-
-        self.diffusion_embedding = DiffusionEmbedding(
-            time_emb_dim, proj_dim=residual_hidden
-        )
-        self.cond_upsampler = CondUpsampler(
-            target_dim=target_dim, cond_length=cond_length
-        )
-        self.residual_layers = nn.ModuleList(
-            [
-                ResidualBlock(
-                    residual_channels=residual_channels,
-                    dilation=2 ** (i % dilation_cycle_length),
-                    hidden_size=residual_hidden,
-                    target_dim=target_dim,
-                )
-                for i in range(residual_layers)
-            ]
-        )
-
-        nn.init.kaiming_normal_(self.input_projection.weight)
-        nn.init.kaiming_normal_(self.skip_projection.weight)
-        nn.init.zeros_(self.output_projection.weight)
-
-    def forward(self, inputs, time, cond):
-        x = self.input_projection(inputs)
-        x = F.leaky_relu(x, 0.4)
-
-        diffusion_step = self.diffusion_embedding(time)
-        cond_up = self.cond_upsampler(cond)
-        skip = []
-        for layer in self.residual_layers:
-            x, skip_connection = layer(x, cond_up, diffusion_step)
-            skip.append(skip_connection)
-
-        x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers))
-        x = self.skip_projection(x)
-        x = F.leaky_relu(x, 0.4)
-        x = self.output_projection(x)
-        return x
-
-
-class GaussianDiffusion(nn.Module):
-    def __init__(
-        self,
-        target_dim,
-        f_hidden_size,
-        conditional_length,
-        beta_end=0.1,
-        diff_steps=100,
-        loss_type="l2",
-        betas=None,
-        beta_schedule="linear",
-        padding=2,
-        residual_channels=8,
-    ):
-        super().__init__()
-        self.dist_args = nn.Linear(
-            in_features=f_hidden_size, out_features=conditional_length
-        )
-        self.denoise_fn = EpsilonTheta(
-            target_dim=target_dim,
-            cond_length=conditional_length,
-            residual_channels=residual_channels,
-            padding=padding,
-        )
-        self.target_dim = target_dim
-        self.__scale = None
-
-        if betas is not None:
-            betas = (
-                betas.detach().cpu().numpy()
-                if isinstance(betas, torch.Tensor)
-                else betas
-            )
-        else:
-            if beta_schedule == "linear":
-                betas = np.linspace(1e-4, beta_end, diff_steps)
-            elif beta_schedule == "quad":
-                betas = np.linspace(1e-4 ** 0.5, beta_end ** 0.5, diff_steps) ** 2
-            elif beta_schedule == "const":
-                betas = beta_end * np.ones(diff_steps)
-            elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
-                betas = 1.0 / np.linspace(diff_steps, 1, diff_steps)
-            elif beta_schedule == "sigmoid":
-                betas = np.linspace(-6, 6, diff_steps)
-                betas = (beta_end - 1e-4) / (np.exp(-betas) + 1) + 1e-4
-            elif beta_schedule == "cosine":
-                betas = cosine_beta_schedule(diff_steps)
-            else:
-                raise NotImplementedError(beta_schedule)
-
-        alphas = 1.0 - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
-
-        (timesteps,) = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.loss_type = loss_type
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer("betas", to_torch(betas))
-        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
-        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer(
-            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
-        )
-        self.register_buffer(
-            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
-        )
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (
-            betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
-        )
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer("posterior_variance", to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer(
-            "posterior_log_variance_clipped",
-            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
-        )
-        self.register_buffer(
-            "posterior_mean_coef1",
-            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
-        )
-        self.register_buffer(
-            "posterior_mean_coef2",
-            to_torch(
-                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
-            ),
-        )
-
-    @property
-    def scale(self):
-        return self.__scale
-
-    @scale.setter
-    def scale(self, scale):
-        self.__scale = scale
-
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, cond, t, clip_denoised: bool):
-        x_recon = self.predict_start_from_noise(
-            x, t=t, noise=self.denoise_fn(x, t, cond=cond)
-        )
-
-        if clip_denoised:
-            x_recon.clamp_(-1.0, 1.0)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
-            x_start=x_recon, x_t=x, t=t
-        )
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, cond, t, clip_denoised=False, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(
-            x=x, cond=cond, t=t, clip_denoised=clip_denoised
-        )
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, cond):
-        device = self.betas.device
-
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-
-        for i in reversed(range(0, self.num_timesteps)):
-            img = self.p_sample(
-                img, cond, torch.full((b,), i, device=device, dtype=torch.long)
-            )
-        return img
-
-    @torch.no_grad()
-    def sample(self, sample_shape=torch.Size(), cond=None):
-        if cond is not None:
-            shape = cond.shape[:-1] + (self.target_dim,)
-            # TODO reshape cond to (B*T, 1, -1)
-        else:
-            shape = sample_shape
-        x_hat = self.p_sample_loop(shape, cond)  # TODO reshape x_hat to (B,T,-1)
-
-        if self.scale is not None:
-            x_hat *= self.scale
-        return x_hat
-
-    @torch.no_grad()
-    def interpolate(self, x1, x2, t=None, lam=0.5):
-        b, *_, device = *x1.shape, x1.device
-        t = default(t, self.num_timesteps - 1)
-
-        assert x1.shape == x2.shape
-
-        t_batched = torch.stack([torch.tensor(t, device=device)] * b)
-        xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
-
-        img = (1 - lam) * xt1 + lam * xt2
-        for i in reversed(range(0, t)):
-            img = self.p_sample(
-                img, torch.full((b,), i, device=device, dtype=torch.long)
-            )
-
-        return img
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        return (
-            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        x_recon = self.denoise_fn(x_noisy, t, cond=cond)
-
-        if self.loss_type == "l1":
-            loss = F.l1_loss(x_recon, noise)
-        elif self.loss_type == "l2":
-            loss = F.mse_loss(x_recon, noise)
-        elif self.loss_type == "huber":
-            loss = F.smooth_l1_loss(x_recon, noise)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
-    def loss(self, x, cond, *args, **kwargs):
-        if self.scale is not None:
-            x /= self.scale
-
-        B, T, _ = x.shape
-
-        time = torch.randint(0, self.num_timesteps, (B * T,), device=x.device).long()
-        loss = self.p_losses(
-            x.reshape(B * T, 1, -1), cond.reshape(B * T, 1, -1), time, *args, **kwargs
-        )
-
-        return loss
diff --git a/probts/model/nn/layers/AutoCorrelation.py b/probts/model/nn/layers/AutoformerModule/AutoCorrelation.py
similarity index 100%
rename from probts/model/nn/layers/AutoCorrelation.py
rename to probts/model/nn/layers/AutoformerModule/AutoCorrelation.py
diff --git a/probts/model/nn/layers/Autoformer_EncDec.py b/probts/model/nn/layers/AutoformerModule/Autoformer_EncDec.py
similarity index 100%
rename from probts/model/nn/layers/Autoformer_EncDec.py
rename to probts/model/nn/layers/AutoformerModule/Autoformer_EncDec.py
diff --git a/probts/model/nn/layers/Conv_Blocks.py b/probts/model/nn/layers/Conv_Blocks.py
deleted file mode 100644
index 96ce3fc..0000000
--- a/probts/model/nn/layers/Conv_Blocks.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class Inception_Block_V1(nn.Module):
-    def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
-        super(Inception_Block_V1, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_kernels = num_kernels
-        kernels = []
-        for i in range(self.num_kernels):
-            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i))
-        self.kernels = nn.ModuleList(kernels)
-        if init_weight:
-            self._initialize_weights()
-
-    def _initialize_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        res_list = []
-        for i in range(self.num_kernels):
-            res_list.append(self.kernels[i](x))
-        res = torch.stack(res_list, dim=-1).mean(-1)
-        return res
-
-
-class Inception_Block_V2(nn.Module):
-    def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
-        super(Inception_Block_V2, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_kernels = num_kernels
-        kernels = []
-        for i in range(self.num_kernels // 2):
-            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[1, 2 * i + 3], padding=[0, i + 1]))
-            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[2 * i + 3, 1], padding=[i + 1, 0]))
-        kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=1))
-        self.kernels = nn.ModuleList(kernels)
-        if init_weight:
-            self._initialize_weights()
-
-    def _initialize_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        res_list = []
-        for i in range(self.num_kernels + 1):
-            res_list.append(self.kernels[i](x))
-        res = torch.stack(res_list, dim=-1).mean(-1)
-        return res
diff --git a/probts/model/nn/layers/ElasTSTModule/ElasTST_backbone.py b/probts/model/nn/layers/ElasTSTModule/ElasTST_backbone.py
new file mode 100644
index 0000000..f31505f
--- /dev/null
+++ b/probts/model/nn/layers/ElasTSTModule/ElasTST_backbone.py
@@ -0,0 +1,282 @@
+__all__ = ['PatchTST_backbone']
+
+# Cell
+from typing import Callable, Optional
+import torch
+from torch import nn
+from torch import Tensor
+import numpy as np
+from einops import rearrange, repeat
+from probts.utils.position_emb import Time_Encoder, sin_cos_encoding
+from .Layers import EncoderLayer
+
+# Cell
+class ElasTST_backbone(nn.Module):
+    def __init__(self, 
+                 l_patch_size: list,
+                 stride: int = None, 
+                 k_patch_size: int = 1, 
+                 in_channels: int = 1,
+                 n_layers: int = 0, 
+                 t_layers: int = 1, 
+                 v_layers: int = 1,
+                 hidden_size: int = 256, 
+                 n_heads: int = 16, 
+                 d_k: Optional[int] = None, 
+                 d_v: Optional[int] = None,
+                 d_inner: int = 256, 
+                 dropout: float = 0.,
+                 rotate: bool = False, 
+                 max_seq_len = 1000, 
+                 theta = 10000, 
+                 learnable_theta = False, 
+                 addv: bool = False,
+                 bin_att: bool = False,
+                 abs_tem_emb: bool = False,
+                 learn_tem_emb: bool = False,
+                 structured_mask: bool = True,
+                 rope_theta_init: str = 'exp',
+                 min_period: float = 1, 
+                 max_period: float = 1000,
+                 patch_share_backbone: bool = True,):
+
+        super().__init__()
+        
+
+        if rotate:
+            print(f'Using Rotary Embedding... [theta init]: {rope_theta_init}, [period range]: [{min_period},{max_period}], [learnable]: {learnable_theta}')
+        print("[Binary Att.]: ", bin_att, " [Learned time emb]: ", learn_tem_emb, " [Abs time emb]: ", abs_tem_emb)
+        print("[Multi Patch Share Backbone]: ", patch_share_backbone)
+        print("[Structured Mask]: ", not structured_mask)
+        # Patching
+        self.l_patch_size = l_patch_size
+        self.k_patch_size = k_patch_size
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_share_backbone = patch_share_backbone
+        self.abs_tem_emb= abs_tem_emb
+
+        self.hidden_size = hidden_size
+        if stride is not None:
+            self.stride = stride
+        else:
+            self.stride = self.l_patch_size
+
+        x_embedder = []
+        final_layer = []
+        backbone = []
+        for p in self.l_patch_size:
+            print(f"=== Patch {p} Branch ===")
+            x_embedder.append(TimePatchEmbed(p, self.k_patch_size, self.in_channels, self.hidden_size, bias=True,stride=p))
+            final_layer.append(MLP_FinalLayer(self.hidden_size, p, self.k_patch_size, self.out_channels))
+            
+            if not patch_share_backbone:
+                backbone.append(DoublyAtt(d_model=self.hidden_size,n_layers=n_layers, t_layers=t_layers, v_layers=v_layers, d_inner=d_inner, n_heads=n_heads, d_k=d_k, d_v=d_v, dropout=dropout, 
+                                    rotate=rotate, max_seq_len=max_seq_len, theta=theta, addv=addv, bin_att=bin_att,
+                                    learnable_theta=learnable_theta, structured_mask=structured_mask,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period))
+            
+        self.x_embedder = nn.ModuleList(x_embedder)
+        self.final_layer = nn.ModuleList(final_layer)
+        
+        if not patch_share_backbone:
+            self.backbone = nn.ModuleList(backbone)
+        else:
+            self.backbone = DoublyAtt(d_model=self.hidden_size,n_layers=n_layers, t_layers=t_layers, v_layers=v_layers, d_inner=d_inner, n_heads=n_heads, d_k=d_k, d_v=d_v, dropout=dropout, 
+                                    rotate=rotate, max_seq_len=max_seq_len, theta=theta, addv=addv, bin_att=bin_att,
+                                    learnable_theta=learnable_theta, structured_mask=structured_mask,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period)
+       
+        self.learn_tem_emb = learn_tem_emb
+        if self.learn_tem_emb:
+            self.learn_time_embedding = Time_Encoder(self.hidden_size)
+
+    def get_patch_num(self, dim_size, len_size, l_patch_size):
+        num_k_patches = int((dim_size - self.k_patch_size)/self.k_patch_size + 1)
+        num_l_patches = int((len_size - l_patch_size)/l_patch_size + 1)
+        return num_k_patches, num_l_patches
+
+
+    def forward(self, past_target, future_placeholder, past_observed_values, future_observed_values, dataset_name=None):                                                                   # z: [bs x nvars x seq_len]
+
+        pred_shape = future_placeholder.shape
+        future_observed_indicator = torch.zeros(future_observed_values.shape).to(future_observed_values.device)
+        
+        x = torch.cat((past_target, future_placeholder), dim=1) # B L+T K
+        
+        past_value_indicator = torch.cat((past_observed_values, future_observed_indicator), dim=1) # B L+T K
+        observed_value_indicator = torch.cat((past_observed_values, future_observed_values), dim=1) # B L+T K
+        
+        pred_list = []
+
+        for idx in range(len(self.l_patch_size)):
+
+            x_p = x.clone()
+            
+            num_k_patches, num_l_patches = self.get_patch_num(x_p.shape[-1], x_p.shape[-2],self.l_patch_size[idx])
+
+            # do patching
+            x_p, past_value_indicator_p, observed_value_indicator_p = self.x_embedder[idx](x_p, past_value_indicator, observed_value_indicator)  # b k l d
+            
+            if self.learn_tem_emb:
+                grid_len = np.arange(num_l_patches, dtype=np.float32)
+                grid_len = torch.tensor(grid_len, requires_grad=False).float().unsqueeze(0).to(x.device)
+                pos_embed = repeat(grid_len, '1 l -> b l', b=pred_shape[0])
+                pos_embed = self.learn_time_embedding(pos_embed) # b l 1 d
+                pos_embed = rearrange(pos_embed, 'b l 1 d -> b 1 l d')
+                x_p = x_p + pos_embed
+            
+            # use a absolute position embedding
+            if self.abs_tem_emb:
+                B, K, L, embed_dim = x_p.shape
+                pos_embed = sin_cos_encoding(B, K, L, embed_dim).float() # b k l d
+                x_p = x_p + pos_embed.to(x_p.device)
+
+            # model
+            if self.patch_share_backbone:
+                x_p = self.backbone(x_p, past_value_indicator_p, observed_value_indicator_p)        # b k l d
+            else:
+                x_p = self.backbone[idx](x_p, past_value_indicator_p, observed_value_indicator_p)        # b k l d
+
+            
+            x_p = self.final_layer[idx](x_p) # b k l p
+
+            x_p = rearrange(x_p, 'b k t p -> b (t p) k')
+
+            x_p = x_p[:,-pred_shape[1]:,:]
+            
+            pred_list.append(x_p.unsqueeze(-1))
+        
+        pred_list = torch.cat(pred_list, dim=-1)
+        multi_patch_mean_res = torch.mean(pred_list, dim=-1)
+
+        return multi_patch_mean_res, pred_list
+
+    
+class DoublyAtt(nn.Module):  
+    def __init__(self, d_model,n_layers, d_inner, n_heads, d_k, d_v, dropout, 
+                 rotate=False, max_seq_len=1024, theta=10000, t_layers=2, v_layers=1,
+                 bin_att=False, addv=False, learnable_theta=False, structured_mask=True,
+                 rope_theta_init='exp',min_period=0.1, max_period=10):
+        super().__init__()
+        # assert n_layers <= (t_layers + v_layers) <= 2*n_layers , "Sum of t_layers and n_layers must be between 1 and 2"  
+        
+        # Configuration based on temporal and variate ratios
+        self.layer_stack = nn.ModuleList()  
+        num_t = t_layers
+        num_v = v_layers
+        num_both = min(t_layers, v_layers)
+
+        num_t = num_t - num_both
+        num_v = num_v - num_both
+        
+        t_count = 0
+        v_count= 0
+        for _ in range(num_t + num_v):
+            if t_count < num_t  :
+                self.layer_stack.append(EncoderLayer(d_model, d_inner, n_heads, d_k, d_v, dropout=dropout, tem_att=True, type_att=False, 
+                         structured_mask=structured_mask, rotate=rotate, max_seq_len=max_seq_len,theta=theta, addv=addv, 
+                         learnable_theta=learnable_theta, bin_att=bin_att,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period))  
+                t_count = t_count + 1
+                print(f"[Encoder Layer {t_count+v_count}] Use tem att")
+            if v_count < num_v:
+                self.layer_stack.append(EncoderLayer(d_model, d_inner, n_heads, d_k, d_v, dropout=dropout, tem_att=False, type_att=True, 
+                         structured_mask=structured_mask, rotate=rotate, max_seq_len=max_seq_len,theta=theta, addv=addv, 
+                         learnable_theta=learnable_theta, bin_att=bin_att,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period))  
+                v_count = v_count + 1
+                print(f"[Encoder Layer {t_count+v_count}] Use var att")
+                
+        for idx in range(num_both):  
+            self.layer_stack.append(EncoderLayer(d_model, d_inner, n_heads, d_k, d_v, dropout=dropout, tem_att=True, type_att=True, 
+                         structured_mask=structured_mask, rotate=rotate, max_seq_len=max_seq_len,theta=theta, addv=addv, 
+                         learnable_theta=learnable_theta, bin_att=bin_att,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period))  
+
+            print(f"[Encoder Layer {idx+t_count+v_count}] Use tem and var att")
+
+    def forward(self, x, past_value_indicator, observed_indicator) -> Tensor:                
+
+        for enc_layer in self.layer_stack:
+            x = enc_layer(x, past_value_indicator=past_value_indicator, observed_indicator=observed_indicator)
+
+        return x  
+
+
+class MLP_FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+    def __init__(self, hidden_size, l_patch_size, k_patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, l_patch_size * k_patch_size * out_channels, bias=True)
+
+
+    def forward(self, x):
+        x = self.norm_final(x)
+        x = self.linear(x)
+        return x
+
+class TimePatchEmbed(nn.Module):
+    """ Time Patch Embedding
+    """
+    def __init__(
+            self,
+            l_patch_size: int = 16,
+            k_patch_size = 1,
+            in_chans: int = 1,
+            embed_dim: int = 768,
+            norm_layer: Optional[Callable] = None,
+            flatten: bool = False,
+            bias: bool = True,
+            # padding_patch = None,
+            stride = None,
+            # strict_img_size: bool = True,
+    ):
+        super().__init__()
+        self.l_patch_size = l_patch_size
+        self.k_patch_size = k_patch_size
+        if stride is None:
+            stride = l_patch_size
+
+        self.flatten = flatten
+
+        padding = 0
+        kernel_size = (l_patch_size,k_patch_size)
+        stride_size = (stride,k_patch_size)
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride_size, bias=bias, padding=padding)
+        self.mask_proj = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=stride_size, bias=False, padding=padding)
+
+        self.mask_proj.weight.data.fill_(1.0)
+
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x, future_mask, obv_mask):
+        '''
+        future_mask: only past values are set to 1
+        obv_mask: past values and values to be predicted are set to 1
+        '''
+        
+        # B, C, K, L = x.shape
+        if len(x.shape) == 3:
+            x = rearrange(x, 'b l k -> b 1 l k')
+            
+        future_mask = rearrange(future_mask, 'b l k -> b 1 l k')
+        obv_mask = rearrange(obv_mask, 'b l k -> b 1 l k')
+            
+        x = self.proj(x)  # B C L K -> B C L' K
+
+        with torch.no_grad():
+            future_mask = self.mask_proj(future_mask)
+            obv_mask = self.mask_proj(obv_mask)
+
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+            future_mask = future_mask.flatten(2).transpose(1, 2)  # NCHW -> NLC
+            obv_mask = obv_mask.flatten(2).transpose(1, 2)  # NCHW -> NLC
+
+        x = self.norm(x)
+
+        x = rearrange(x, 'b d l k -> b k l d')
+        future_mask = rearrange(future_mask, 'b 1 l k -> b k l')
+        obv_mask = rearrange(obv_mask, 'b 1 l k -> b k l')
+        return x, future_mask, obv_mask
+
diff --git a/probts/model/nn/layers/ElasTSTModule/Layers.py b/probts/model/nn/layers/ElasTSTModule/Layers.py
new file mode 100644
index 0000000..d6467d5
--- /dev/null
+++ b/probts/model/nn/layers/ElasTSTModule/Layers.py
@@ -0,0 +1,100 @@
+import torch.nn as nn
+import sys
+import torch
+from .SubLayers import  PositionwiseFeedForward, MultiHeadAttention_tem_bias, MultiHeadAttention_type_bias
+from einops import rearrange, repeat
+
+
+PAD = 0
+
+def get_attn_key_pad_mask_K(past_value_indicator, observed_indicator , transpose=False, structured_mask=False):
+    """ For masking out the padding part of key sequence. 
+    input: mask: transpose=False: [b k l]
+    """
+
+    if structured_mask:
+        mask = past_value_indicator
+    else:
+        mask = observed_indicator
+
+
+    if transpose:
+        mask = rearrange(mask, 'b l k -> b k l')
+    padding_mask = repeat(mask, 'b k l1 -> b k l2 l1', l2=mask.shape[-1]).eq(PAD)
+
+    return padding_mask
+
+class EncoderLayer(nn.Module):
+    """ Compose with two layers """
+
+    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1, 
+                 tem_att=True, type_att=False, structured_mask=True, 
+                 rotate=False, max_seq_len=100, theta=10000,
+                 addv=False, learnable_theta=False, bin_att=False,
+                 rope_theta_init='exp',min_period=0.1, max_period=10):
+        super(EncoderLayer, self).__init__()
+
+        self.structured_mask = structured_mask
+        self.tem_att = tem_att
+        self.type_att = type_att
+
+        if tem_att:
+            self.slf_tem_attn = MultiHeadAttention_tem_bias(
+                n_head, d_model, d_k, d_v, dropout=dropout, rotate=rotate, max_seq_len=max_seq_len, theta=theta, addv=addv, 
+                learnable_theta=learnable_theta, bin_att=bin_att,rope_theta_init=rope_theta_init, min_period=min_period, max_period=max_period)
+
+        if type_att:
+            self.slf_type_attn = MultiHeadAttention_type_bias(
+                n_head, d_model, d_k, d_v, dropout=dropout, rotate=False, max_seq_len=max_seq_len, bin_att=bin_att)
+
+
+        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)
+        
+        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
+
+    def forward(self, input, past_value_indicator=None, observed_indicator=None):
+        # time attention
+        # [B, K, L, D]
+        if self.tem_att:
+            tem_mask = get_attn_key_pad_mask_K(past_value_indicator=past_value_indicator, observed_indicator=observed_indicator, transpose=False, structured_mask=self.structured_mask)
+            tem_output = self.layer_norm(input)
+            
+            tem_output, enc_tem_attn = self.slf_tem_attn(
+                tem_output, tem_output, tem_output, mask=tem_mask) 
+            
+            tem_output = tem_output + input
+        else:
+            tem_output = input
+        
+        tem_output = rearrange(tem_output, 'b k l d -> b l k d')
+
+        
+        # type attention
+        # [B, L, K, D]
+        if self.type_att:
+            type_mask = get_attn_key_pad_mask_K(past_value_indicator=past_value_indicator, observed_indicator=observed_indicator, transpose=True, structured_mask=self.structured_mask)
+            
+            type_output = self.layer_norm(tem_output)
+            
+            type_output, enc_type_attn = self.slf_type_attn(
+                type_output, type_output, type_output, mask=type_mask) 
+            
+            enc_output = type_output + tem_output
+        else:
+            enc_output = tem_output
+            
+        # FFNN
+        output = self.layer_norm(enc_output)
+        
+        output = self.pos_ffn(output)
+
+        output = output + enc_output
+        
+        output = rearrange(output, 'b l k d -> b k l d')
+        
+        # optional
+        output = self.layer_norm(output)
+
+        return output #, enc_tem_attn, enc_type_attn
+
+
diff --git a/probts/model/nn/layers/ElasTSTModule/Modules.py b/probts/model/nn/layers/ElasTSTModule/Modules.py
new file mode 100644
index 0000000..8fc5fac
--- /dev/null
+++ b/probts/model/nn/layers/ElasTSTModule/Modules.py
@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from .TRoPE import RotaryEmbedding
+
+class ScaledDotProductAttention(nn.Module):
+    """ Scaled Dot-Product Attention """
+
+    def __init__(self, temperature, attn_dropout=0.2):
+        super().__init__()
+
+        self.temperature = temperature
+        self.dropout = nn.Dropout(attn_dropout)
+
+    def forward(self, q, k, v, mask=None):
+        attn = torch.bmm(q / self.temperature, k.transpose(-2, -1))
+        
+        if mask is not None and mask.dim() == 5:
+            mask = mask.transpose(2, 4)
+
+        if mask is not None:
+            attn = attn.masked_fill(mask, -1e9)
+
+        attn = self.dropout(F.softmax(attn, dim=-1))
+        output = torch.bmm(attn, v)
+
+        return output, attn
+
+
+class ScaledDotProductAttention_bias(nn.Module):
+
+    def __init__(self, d_model, n_head, d_k, d_v, temperature, 
+                 attn_dropout=0.2, rotate=False, max_seq_len=100, 
+                 theta=10000, addv=False, learnable_theta=False, 
+                 bin_att=False,rope_theta_init='exp',
+                 min_period=0.1, max_period=10):
+        super().__init__()
+        
+        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
+        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
+        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
+
+        self.temperature = temperature
+        self.dropout = nn.Dropout(attn_dropout)
+        self.n_head = n_head
+        self.bin_att = bin_att
+        self.rotate = rotate
+        self.addv = addv
+        self.trope = RotaryEmbedding(d_v, max_seq_len,base=theta, learnable=learnable_theta,init=rope_theta_init,min_period=min_period, max_period=max_period)
+
+        if self.bin_att:
+            self.alpha = nn.Parameter(torch.zeros([1,1,n_head,1,1]))
+            self.beta = nn.Parameter(torch.zeros([1,1,n_head,1,1]))
+
+    def forward(self, q, k, v, mask):
+        # input: [B,K,H,LQ,LK] for temporal, [B,L,H,Kq,Kk] for category
+        
+        # [B,K,L,H,D]
+        q = rearrange(self.w_qs(q), 'b k l (n d) -> b k n l d', n=self.n_head)
+        k = rearrange(self.w_ks(k), 'b k l (n d) -> b k n d l', n=self.n_head)
+        v = rearrange(self.w_vs(v), 'b k l (n d) -> b k n l d', n=self.n_head)
+        
+        B, K, N, L, D = q.shape
+        if self.rotate:
+            xq = rearrange(q, 'b k n l d -> (b k n) l d')
+            xk = rearrange(k, 'b k n d l -> (b k n) l d')
+            xv = rearrange(v, 'b k n l d -> (b k n) l d')
+
+            xq, xk, xv = self.trope(xq, xk, xv)
+
+            attn = torch.matmul(xq, xk.transpose(1, 2)) / self.temperature
+            attn = rearrange(attn, '(b k n) l t -> b k n l t', b=B, k=K,n=N)
+            if self.addv:
+                v = rearrange(xv, '(b k n) l d -> b k n l d', b=B, k=K,n=N)
+        else:
+            attn = torch.matmul(q , k) / self.temperature
+
+        if self.bin_att:
+            self_mask = torch.eye(L).to(mask.device)
+            self_mask = repeat(self_mask, 'l t -> b k n l t', b=B, k=K,n=N)
+
+            attn = attn + self_mask * self.alpha + (1-self_mask) * self.beta
+
+        if mask is not None:
+            if attn.dim() > mask.dim():
+                mask = mask.unsqueeze(2).expand(attn.shape)
+            attn = attn.masked_fill(mask, -1e9)
+            
+
+        attn = self.dropout(F.softmax(attn, dim=-1))
+
+        v = torch.matmul(attn, v)
+
+        v = rearrange(v, 'b k n l d -> b k l (n d)')
+
+        # sys.exit(0)
+        return v, attn
+    
+class Attention(nn.Module):
+
+    def __init__(self, hin_d, d_model):
+        super().__init__()
+
+        self.linear = nn.Linear(d_model, hin_d)
+        self.W = nn.Linear(hin_d,1, bias=False)
+        
+    def forward(self, x, mask=None, mask_value=-1e30):
+        # [B,K,L,D]
+        
+        # map directly
+        attn = self.W(torch.tanh(self.linear(x))) # [B,K,L,1]
+        
+        if mask is not None:
+            attn = mask * attn + (1-mask)*mask_value
+            
+        attn = F.softmax(attn, dim=-2)
+        
+        x = torch.matmul(x.transpose(-1, -2), attn).squeeze(-1) # [B,K,D,1]
+
+        return x, attn
\ No newline at end of file
diff --git a/probts/model/nn/layers/ElasTSTModule/SubLayers.py b/probts/model/nn/layers/ElasTSTModule/SubLayers.py
new file mode 100644
index 0000000..e1a5b85
--- /dev/null
+++ b/probts/model/nn/layers/ElasTSTModule/SubLayers.py
@@ -0,0 +1,78 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import sys
+
+from .Modules import ScaledDotProductAttention_bias
+
+class MultiHeadAttention_tem_bias(nn.Module):
+    """ Multi-Head Attention module """
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1, rotate=False, max_seq_len=100, theta=10000, addv=False, 
+                 learnable_theta=False, bin_att=False,rope_theta_init='exp',min_period=0.1, max_period=10):
+        super().__init__()
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.fc = nn.Linear(d_v * n_head, d_model)
+
+        self.attention = ScaledDotProductAttention_bias(d_model, n_head, d_k, d_v, temperature=d_k ** 0.5, 
+                                                        attn_dropout=dropout, rotate=rotate, max_seq_len=max_seq_len, 
+                                                        theta=theta, addv=addv, learnable_theta=learnable_theta, bin_att=bin_att, 
+                                                        rope_theta_init=rope_theta_init,min_period=min_period, max_period=max_period)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, q, k, v, mask=None):
+        # event_matrix [B,L,K]
+
+        # [B,K,H,Lq,Lk]
+        output, attn = self.attention(q, k, v, mask=mask) # [B,K,H,L,D]
+
+        output = self.dropout(self.fc(output))
+
+        return output, attn
+
+
+class MultiHeadAttention_type_bias(nn.Module):
+    """ Multi-Head Attention module """
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1, rotate=False, max_seq_len=1024, bin_att=False):
+        super().__init__()
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.fc = nn.Linear(d_v * n_head, d_model)
+        self.attention = ScaledDotProductAttention_bias(d_model, n_head, d_k, d_v, temperature=d_k ** 0.5, attn_dropout=dropout, rotate=False, max_seq_len=max_seq_len, bin_att=bin_att)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, q, k, v, mask=None):
+        # [B,L,K,D]
+        output, attn = self.attention(q, k, v, mask=mask) 
+
+        output = self.dropout(self.fc(output))
+
+        return output, attn
+
+
+class PositionwiseFeedForward(nn.Module):
+    """ Two-layer position-wise feed-forward neural network. """
+
+    def __init__(self, d_in, d_hid, dropout=0.1):
+        super().__init__()
+        self.w_1 = nn.Linear(d_in, d_hid)
+        self.w_2 = nn.Linear(d_hid, d_in)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = F.gelu(self.w_1(x))
+        x = self.dropout(x)
+        x = self.w_2(x)
+        x = self.dropout(x)
+
+        return x
+
+
diff --git a/probts/model/nn/layers/ElasTSTModule/TRoPE.py b/probts/model/nn/layers/ElasTSTModule/TRoPE.py
new file mode 100644
index 0000000..9361175
--- /dev/null
+++ b/probts/model/nn/layers/ElasTSTModule/TRoPE.py
@@ -0,0 +1,105 @@
+import torch
+from typing import Tuple
+import torch
+import torch.nn as nn
+import numpy as np
+import sys
+
+class RotaryEmbedding(nn.Module):  
+    def __init__(self, dim: int, seq_len: int, base: float = 10000.0, learnable=False, init="exp",min_period=0.01, max_period=1000):  
+        super(RotaryEmbedding, self).__init__()  
+        if init == 'linear':
+            theta = get_linear_period(min_period, max_period, dim)
+        elif init == 'uniform':
+            theta = torch.ones([dim//2])
+            periods = torch.nn.init.uniform_(theta, a=min_period, b=max_period)
+            theta = 2 * np.pi / periods
+        elif init == 'exp':
+            theta = get_exp_period(min_period, max_period, dim)
+        elif init == 'rope':
+            theta = 1.0 / (base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+        else:
+            print("invalid theta init")
+            sys.exit(0)
+
+        if learnable:  
+            self.freqs = nn.Parameter(theta)
+        else:  
+            self.register_buffer('freqs', torch.tensor(theta))
+        
+        self.dim = dim  
+        self.seq_len = seq_len  
+        self.learnable = learnable  
+
+    def forward(self, xq: torch.Tensor, xk: torch.Tensor, xv: torch.Tensor):
+        L = xq.shape[-2]
+        t = torch.arange(L, device=xq.device)
+            
+        freqs = torch.outer(t, self.freqs).float()  # m * \theta
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+        
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 2)
+        xv_ = xv.float().reshape(*xv.shape[:-1], -1, 2)
+    
+        xq_ = torch.view_as_complex(xq_).to(xq.device)
+        xk_ = torch.view_as_complex(xk_).to(xq.device)
+        xv_ = torch.view_as_complex(xv_).to(xq.device)
+        
+        # rotate and then map to real number field
+        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(2).to(xq.device)
+        xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2).to(xq.device)
+        xv_out = torch.view_as_real(xv_ * freqs_cis).flatten(2).to(xq.device)
+        return xq_out.type_as(xq), xk_out.type_as(xk), xv_out.type_as(xv)
+
+
+def get_linear_period(min_period, max_period, dim):
+    i = torch.arange(0, dim, 2)[: (dim // 2)]
+
+    periods = min_period + ((max_period - min_period) / dim )  * i
+    theta = 2 * np.pi / periods  
+    return theta
+
+def get_exp_period(min_period, max_period, dim):
+    i = torch.arange(0, dim, 2)[: (dim // 2)]
+    max_theta = 2 * np.pi / min_period
+    min_theta = 2 * np.pi / max_period
+    alpha = np.log(max_theta/min_theta) * (1/(dim-2))
+    thetas = max_theta * np.exp(-alpha * i)
+    return thetas
+
+# generate rotation matrix
+def precompute_freqs_cis(dim: int, seq_len: int, theta: float = 10000.0):
+    
+    # rotate \theta_i
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    # generate token indexes t = [0, 1,..., seq_len-1]
+    t = torch.arange(seq_len, device=freqs.device)
+    # freqs.shape = [seq_len, dim // 2] 
+    freqs = torch.outer(t, freqs).float()  # m * \theta
+
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs) 
+    return freqs_cis
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    xv: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 2)
+    xv_ = xv.float().reshape(*xv.shape[:-1], -1, 2)
+    
+    freqs_cis = freqs_cis.to(xq.device)
+
+    xq_ = torch.view_as_complex(xq_).to(xq.device)
+    xk_ = torch.view_as_complex(xk_).to(xq.device)
+    xv_ = torch.view_as_complex(xv_).to(xq.device)
+    
+    # rotate and then map to real number field
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(2).to(xq.device)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2).to(xq.device)
+    xv_out = torch.view_as_real(xv_ * freqs_cis).flatten(2).to(xq.device)
+    return xq_out.type_as(xq), xk_out.type_as(xk), xv_out.type_as(xv)
+
diff --git a/probts/model/nn/layers/ElasTSTModule/__init__.py b/probts/model/nn/layers/ElasTSTModule/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/probts/model/nn/layers/Moirai_backbone.py b/probts/model/nn/layers/Moirai_backbone.py
deleted file mode 100644
index 588a124..0000000
--- a/probts/model/nn/layers/Moirai_backbone.py
+++ /dev/null
@@ -1,513 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from uni2ts
-# - Source: https://github.com/SalesforceAIResearch/uni2ts
-# - Paper: Unified Training of Universal Time Series Forecasting Transformers
-# - License: Apache License 2.0
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import math
-from contextlib import contextmanager
-from copy import deepcopy
-from typing import Any, Generator, Optional
-
-import lightning as L
-import torch
-from einops import rearrange, reduce, repeat
-from jaxtyping import Bool, Float, Int
-from torch.distributions import Distribution
-
-from uni2ts.common.torch_util import safe_div
-from uni2ts.loss.packed import PackedNLLLoss as _PackedNLLLoss
-from uni2ts.model.moirai.module import MoiraiModule
-from uni2ts.module.packed_scaler import PackedNOPScaler, PackedStdScaler
-
-
-class SampleNLLLoss(_PackedNLLLoss):
-    def reduce_loss(
-        self,
-        loss: Float[torch.Tensor, "batch seq_len #dim"],
-        prediction_mask: Optional[Bool[torch.Tensor, "batch seq_len"]],
-        observed_mask: Optional[Bool[torch.Tensor, "batch seq_len #dim"]],
-        sample_id: Optional[Int[torch.Tensor, "batch seq_len"]],
-        variate_id: Optional[Int[torch.Tensor, "batch seq_len"]],
-    ) -> Float[torch.Tensor, "batch"]:
-        id_mask = torch.logical_and(
-            torch.eq(sample_id.unsqueeze(-1), sample_id.unsqueeze(-2)),
-            torch.eq(variate_id.unsqueeze(-1), variate_id.unsqueeze(-2)),
-        )
-        mask = prediction_mask.unsqueeze(-1) * observed_mask
-        tobs = reduce(
-            id_mask
-            * reduce(
-                mask,
-                "... seq dim -> ... 1 seq",
-                "sum",
-            ),
-            "... seq1 seq2 -> ... seq1 1",
-            "sum",
-        )
-        loss = safe_div(loss, tobs)
-        return (loss * mask).sum(dim=(-1, -2))
-
-
-class MoiraiBackbone(L.LightningModule):
-    def __init__(
-        self,
-        prediction_length: int,
-        target_dim: int,
-        context_length: int,
-        module_kwargs: Optional[dict[str, Any]] = None,
-        module: Optional[MoiraiModule] = None,
-        patch_size: int | str = "auto",
-        num_samples: int = 100,
-        scaling: bool = True,
-    ):
-        assert (module is not None) or (
-            module_kwargs is not None
-        ), "if module is not provided, module_kwargs is required"
-        super().__init__()
-        self.save_hyperparameters(ignore=["module"])
-        self.module = MoiraiModule(**module_kwargs) if module is None else module
-        self.module.scaling = scaling
-        self.module.scaler = PackedStdScaler() if scaling else PackedNOPScaler()
-        self.per_sample_loss_func = SampleNLLLoss()
-
-    @contextmanager
-    def hparams_context(
-        self,
-        prediction_length: Optional[int] = None,
-        target_dim: Optional[int] = None,
-        context_length: Optional[int] = None,
-        patch_size: Optional[int | str] = None,
-        num_samples: Optional[int] = None,
-    ) -> Generator["MoiraiForecast", None, None]:
-        kwargs = {
-            "prediction_length": prediction_length,
-            "target_dim": target_dim,
-            "context_length": context_length,
-            "patch_size": patch_size,
-            "num_samples": num_samples,
-        }
-        old_hparams = deepcopy(self.hparams)
-        for kw, arg in kwargs.items():
-            if arg is not None:
-                self.hparams[kw] = arg
-
-        yield self
-
-        for kw in kwargs:
-            self.hparams[kw] = old_hparams[kw]
-
-    @property
-    def past_length(self) -> int:
-        return (
-            self.hparams.context_length + self.hparams.prediction_length
-            if self.hparams.patch_size == "auto"
-            else self.hparams.context_length
-        )
-
-    def context_token_length(self, patch_size: int) -> int:
-        return math.ceil(self.hparams.context_length / patch_size)
-
-    def prediction_token_length(self, patch_size) -> int:
-        return math.ceil(self.hparams.prediction_length / patch_size)
-
-    @property
-    def max_patch_size(self) -> int:
-        return max(self.module.patch_sizes)
-
-    def forward(
-        self,
-        past_target: Float[torch.Tensor, "batch past_time tgt"],
-        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
-        past_is_pad: Bool[torch.Tensor, "batch past_time"],
-        num_samples: Optional[int] = None,
-    ) -> Float[torch.Tensor, "batch sample future_time *tgt"]:
-        if self.hparams.patch_size == "auto":
-            val_loss = []
-            preds = []
-            for patch_size in self.module.patch_sizes:
-                val_loss.append(
-                    self._val_loss(
-                        patch_size=patch_size,
-                        target=past_target[..., : self.past_length, :],
-                        observed_target=past_observed_target[
-                            ..., : self.past_length, :
-                        ],
-                        is_pad=past_is_pad[..., : self.past_length]
-                    )
-                )
-                distr = self._get_distr(
-                    patch_size,
-                    past_target[..., -self.hparams.context_length :, :],
-                    past_observed_target[..., -self.hparams.context_length :, :],
-                    past_is_pad[..., -self.hparams.context_length :]
-                )
-                preds.append(
-                    self._format_preds(
-                        patch_size,
-                        distr.sample(
-                            torch.Size((num_samples or self.hparams.num_samples,))
-                        ),
-                        past_target.shape[-1],
-                    )
-                )
-            val_loss = torch.stack(val_loss)
-            preds = torch.stack(preds)
-            idx = val_loss.argmin(dim=0)
-            return preds[idx, torch.arange(len(idx), device=idx.device)]
-        else:
-            distr = self._get_distr(
-                self.hparams.patch_size,
-                past_target[..., -self.hparams.context_length :, :],
-                past_observed_target[..., -self.hparams.context_length :, :],
-                past_is_pad[..., -self.hparams.context_length :],
-            )
-            preds = distr.sample(torch.Size((num_samples or self.hparams.num_samples,)))
-            return self._format_preds(
-                self.hparams.patch_size, preds, past_target.shape[-1]
-            )
-
-    def _val_loss(
-        self,
-        patch_size: int,
-        target: Float[torch.Tensor, "batch time tgt"],
-        observed_target: Bool[torch.Tensor, "batch time tgt"],
-        is_pad: Bool[torch.Tensor, "batch time"]
-    ) -> Float[torch.Tensor, "batch"]:
-        # convert format
-        (
-            target,
-            observed_mask,
-            sample_id,
-            time_id,
-            variate_id,
-            prediction_mask,
-        ) = self._convert(
-            patch_size,
-            past_target=target[..., : self.hparams.context_length, :],
-            past_observed_target=observed_target[..., : self.hparams.context_length, :],
-            past_is_pad=is_pad[..., : self.hparams.context_length],
-            future_target=target[..., self.hparams.context_length :, :],
-            future_observed_target=observed_target[
-                ..., self.hparams.context_length :, :
-            ],
-            future_is_pad=is_pad[..., self.hparams.context_length :]
-        )
-        # get predictions
-        distr = self.module(
-            target,
-            observed_mask,
-            sample_id,
-            time_id,
-            variate_id,
-            prediction_mask,
-            torch.ones_like(time_id, dtype=torch.long) * patch_size,
-        )
-        val_loss = self.per_sample_loss_func(
-            pred=distr,
-            target=target,
-            prediction_mask=prediction_mask,
-            observed_mask=observed_mask,
-            sample_id=sample_id,
-            variate_id=variate_id,
-        )
-        return val_loss
-
-    def _get_distr(
-        self,
-        patch_size: int,
-        past_target: Float[torch.Tensor, "batch past_time tgt"],
-        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
-        past_is_pad: Bool[torch.Tensor, "batch past_time"]
-    ) -> Distribution:
-        # convert format
-        (
-            target,
-            observed_mask,
-            sample_id,
-            time_id,
-            variate_id,
-            prediction_mask,
-        ) = self._convert(
-            patch_size,
-            past_target,
-            past_observed_target,
-            past_is_pad
-        )
-        # get predictions
-        distr = self.module(
-            target,
-            observed_mask,
-            sample_id,
-            time_id,
-            variate_id,
-            prediction_mask,
-            torch.ones_like(time_id, dtype=torch.long) * patch_size,
-        )
-        return distr
-
-    @staticmethod
-    def _patched_seq_pad(
-        patch_size: int,
-        x: torch.Tensor,
-        dim: int,
-        left: bool = True,
-        value: Optional[float] = None,
-    ) -> torch.Tensor:
-        if dim >= 0:
-            dim = -x.ndim + dim
-        pad_length = -x.size(dim) % patch_size
-        if left:
-            pad = (pad_length, 0)
-        else:
-            pad = (0, pad_length)
-        pad = (0, 0) * (abs(dim) - 1) + pad
-        return torch.nn.functional.pad(x, pad, value=value)
-
-    def _generate_time_id(
-        self,
-        patch_size: int,
-        past_observed_target: Bool[torch.Tensor, "batch past_seq tgt"],
-        future_target: Float[torch.Tensor, "batch future_seq tgt"],
-    ) -> tuple[
-        Int[torch.Tensor, "batch past_token"], Int[torch.Tensor, "batch future_token"]
-    ]:
-        past_seq_id = reduce(
-            self._patched_seq_pad(patch_size, past_observed_target, -2, left=True),
-            "... (seq patch) dim -> ... seq",
-            "max",
-            patch=patch_size,
-        )
-        past_seq_id = torch.clamp(past_seq_id.cumsum(dim=-1) - 1, min=0)
-        batch_shape = " ".join(map(str, past_observed_target.shape[:-2]))
-        future_seq_id = (
-            repeat(
-                torch.arange(
-                    math.ceil(future_target.shape[-2] / patch_size),
-                    device=past_observed_target.device,
-                ),
-                f"prediction -> {batch_shape} prediction",
-            )
-            + past_seq_id.max(dim=-1, keepdim=True).values
-            + 1
-        )
-        past_seq_id = past_seq_id.to(dtype=torch.int32)
-        future_seq_id = future_seq_id.to(dtype=torch.int32)
-        return past_seq_id, future_seq_id
-
-    def _convert(
-        self,
-        patch_size: int,
-        past_target: Float[torch.Tensor, "batch past_time tgt"],
-        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
-        past_is_pad: Bool[torch.Tensor, "batch past_time"],
-        future_target: Optional[Float[torch.Tensor, "batch future_time tgt"]] = None,
-        future_observed_target: Optional[
-            Bool[torch.Tensor, "batch future_time tgt"]
-        ] = None,
-        future_is_pad: Optional[Bool[torch.Tensor, "batch future_time"]] = None
-    ) -> tuple[
-        Float[torch.Tensor, "batch combine_seq patch"],  # target
-        Bool[torch.Tensor, "batch combine_seq patch"],  # observed_mask
-        Int[torch.Tensor, "batch combine_seq"],  # sample_id
-        Int[torch.Tensor, "batch combine_seq"],  # time_id
-        Int[torch.Tensor, "batch combine_seq"],  # variate_id
-        Bool[torch.Tensor, "batch combine_seq"],  # prediction_mask
-    ]:
-        batch_shape = past_target.shape[:-2]
-        device = past_target.device
-
-        target = []
-        observed_mask = []
-        sample_id = []
-        time_id = []
-        variate_id = []
-        prediction_mask = []
-        dim_count = 0
-
-        if future_target is None:
-            future_target = torch.zeros(
-                batch_shape
-                + (
-                    self.hparams.prediction_length,
-                    past_target.shape[-1],
-                ),
-                dtype=past_target.dtype,
-                device=device,
-            )
-        
-        past_seq_id, future_seq_id = self._generate_time_id(
-            patch_size, past_observed_target, future_target
-        )
-
-        target.extend(
-            [
-                torch.nn.functional.pad(
-                    rearrange(
-                        self._patched_seq_pad(patch_size, past_target, -2, left=True),
-                        "... (seq patch) dim -> ... (dim seq) patch",
-                        patch=patch_size,
-                    ),
-                    (0, self.max_patch_size - patch_size),
-                ),
-                torch.nn.functional.pad(
-                    rearrange(
-                        self._patched_seq_pad(
-                            patch_size, future_target, -2, left=False
-                        ),
-                        "... (seq patch) dim -> ... (dim seq) patch",
-                        patch=patch_size,
-                    ),
-                    (0, self.max_patch_size - patch_size),
-                ),
-            ]
-        )
-        if future_observed_target is None:
-            future_observed_target = torch.ones(
-                batch_shape
-                + (
-                    self.hparams.prediction_length,
-                    past_observed_target.shape[-1],
-                ),
-                dtype=torch.bool,
-                device=device,
-            )
-        observed_mask.extend(
-            [
-                torch.nn.functional.pad(
-                    rearrange(
-                        self._patched_seq_pad(
-                            patch_size, past_observed_target, -2, left=True
-                        ),
-                        "... (seq patch) dim -> ... (dim seq) patch",
-                        patch=patch_size,
-                    ),
-                    (0, self.max_patch_size - patch_size),
-                ),
-                torch.nn.functional.pad(
-                    rearrange(
-                        self._patched_seq_pad(
-                            patch_size, future_observed_target, -2, left=False
-                        ),
-                        "... (seq patch) dim -> ... (dim seq) patch",
-                        patch=patch_size,
-                    ),
-                    (0, self.max_patch_size - patch_size),
-                ),
-            ]
-        )
-        if future_is_pad is None:
-            future_is_pad = torch.zeros(
-                batch_shape + (self.hparams.prediction_length,),
-                dtype=torch.long,
-                device=device,
-            )
-        sample_id.extend(
-            [
-                repeat(
-                    reduce(
-                        (
-                            self._patched_seq_pad(
-                                patch_size, past_is_pad, -1, left=True, value=1
-                            )
-                            == 0
-                        ).int(),
-                        "... (seq patch) -> ... seq",
-                        "max",
-                        patch=patch_size,
-                    ),
-                    "... seq -> ... (dim seq)",
-                    dim=past_target.shape[-1],
-                ),
-                repeat(
-                    reduce(
-                        (
-                            self._patched_seq_pad(
-                                patch_size, future_is_pad, -1, left=False, value=1
-                            )
-                            == 0
-                        ).int(),
-                        "... (seq patch) -> ... seq",
-                        "max",
-                        patch=patch_size,
-                    ),
-                    "... seq -> ... (dim seq)",
-                    dim=past_target.shape[-1],
-                ),
-            ]
-        )
-        time_id.extend(
-            [past_seq_id] * past_target.shape[-1]
-            + [future_seq_id] * past_target.shape[-1]
-        )
-        variate_id.extend(
-            [
-                repeat(
-                    torch.arange(past_target.shape[-1], device=device) + dim_count,
-                    f"dim -> {' '.join(map(str, batch_shape))} (dim past)",
-                    past=self.context_token_length(patch_size),
-                ),
-                repeat(
-                    torch.arange(past_target.shape[-1], device=device) + dim_count,
-                    f"dim -> {' '.join(map(str, batch_shape))} (dim future)",
-                    # future=self.prediction_token_length(patch_size),
-                    future = math.ceil(future_target.shape[-2] / patch_size)
-                ),
-            ]
-        )
-        dim_count += past_target.shape[-1]
-        prediction_mask.extend(
-            [
-                torch.zeros(
-                    batch_shape
-                    + (self.context_token_length(patch_size) * past_target.shape[-1],),
-                    dtype=torch.bool,
-                    device=device,
-                ),
-                torch.ones(
-                    batch_shape
-                    + (
-                        # self.prediction_token_length(patch_size)
-                        math.ceil(future_target.shape[-2] / patch_size)
-                        * past_target.shape[-1],
-                    ),
-                    dtype=torch.bool,
-                    device=device,
-                ),
-            ]
-        )
-
-        target = torch.cat(target, dim=-2)
-        observed_mask = torch.cat(observed_mask, dim=-2)
-        sample_id = torch.cat(sample_id, dim=-1)
-        time_id = torch.cat(time_id, dim=-1)
-        variate_id = torch.cat(variate_id, dim=-1)
-        prediction_mask = torch.cat(prediction_mask, dim=-1)
-        return (
-            target,
-            observed_mask,
-            sample_id,
-            time_id,
-            variate_id,
-            prediction_mask,
-        )
-
-    def _format_preds(
-        self,
-        patch_size: int,
-        preds: Float[torch.Tensor, "sample batch combine_seq patch"],
-        target_dim: int,
-    ) -> Float[torch.Tensor, "batch sample future_time *tgt"]:
-        start = target_dim * self.context_token_length(patch_size)
-        end = start + target_dim * self.prediction_token_length(patch_size)
-        preds = preds[..., start:end, :patch_size]
-        preds = rearrange(
-            preds,
-            "sample ... (dim seq) patch -> ... sample (seq patch) dim",
-            dim=target_dim,
-        )[..., : self.hparams.prediction_length, :]
-        return preds.squeeze(-1)
\ No newline at end of file
diff --git a/probts/model/nn/layers/PatchTST_backbone.py b/probts/model/nn/layers/PatchTSTModule/PatchTST_backbone.py
similarity index 99%
rename from probts/model/nn/layers/PatchTST_backbone.py
rename to probts/model/nn/layers/PatchTSTModule/PatchTST_backbone.py
index 3394a55..98a0e79 100644
--- a/probts/model/nn/layers/PatchTST_backbone.py
+++ b/probts/model/nn/layers/PatchTSTModule/PatchTST_backbone.py
@@ -18,7 +18,7 @@
 
 #from collections import OrderedDict
 from .PatchTST_layers import *
-from .RevIN import RevIN
+from ..RevIN import RevIN
 
 # Cell
 class PatchTST_backbone(nn.Module):
diff --git a/probts/model/nn/layers/PatchTST_layers.py b/probts/model/nn/layers/PatchTSTModule/PatchTST_layers.py
similarity index 100%
rename from probts/model/nn/layers/PatchTST_layers.py
rename to probts/model/nn/layers/PatchTSTModule/PatchTST_layers.py
diff --git a/probts/model/nn/layers/TSMixer_layers.py b/probts/model/nn/layers/TSMixer_layers.py
new file mode 100644
index 0000000..e632941
--- /dev/null
+++ b/probts/model/nn/layers/TSMixer_layers.py
@@ -0,0 +1,395 @@
+from __future__ import annotations
+from collections.abc import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+import sys
+
+class TimeBatchNorm2d(nn.BatchNorm1d):
+    """A batch normalization layer that normalizes over the last two dimensions of a
+    sequence in PyTorch, mimicking Keras behavior.
+
+    This class extends nn.BatchNorm1d to apply batch normalization across time and
+    feature dimensions.
+
+    Attributes:
+        num_time_steps (int): Number of time steps in the input.
+        num_channels (int): Number of channels in the input.
+    """
+
+    def __init__(self, normalized_shape: tuple[int, int]):
+        """Initializes the TimeBatchNorm2d module.
+
+        Args:
+            normalized_shape (tuple[int, int]): A tuple (num_time_steps, num_channels)
+                representing the shape of the time and feature dimensions to normalize.
+        """
+        num_time_steps, num_channels = normalized_shape
+        
+        super().__init__(num_channels * num_time_steps)
+        self.num_time_steps = num_time_steps
+        self.num_channels = num_channels
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Applies the batch normalization over the last two dimensions of the input tensor.
+
+        Args:
+            x (Tensor): A 3D tensor with shape (N, S, C), where N is the batch size,
+                S is the number of time steps, and C is the number of channels.
+
+        Returns:
+            Tensor: A 3D tensor with batch normalization applied over the last two dims.
+
+        Raises:
+            ValueError: If the input tensor is not 3D.
+        """
+        if x.ndim != 3:
+            raise ValueError(f"Expected 3D input tensor, but got {x.ndim}D tensor instead.")
+
+        # Reshaping input to combine time and feature dimensions for normalization
+        x = x.reshape(x.shape[0], -1, 1)
+
+        # Applying batch normalization
+        x = super().forward(x)
+
+        # Reshaping back to original dimensions (N, S, C)
+        x = x.reshape(x.shape[0], self.num_time_steps, self.num_channels)
+
+        return x
+
+
+class FeatureMixing(nn.Module):
+    """A module for feature mixing with flexibility in normalization and activation.
+
+    This module provides options for batch normalization before or after mixing features,
+    uses dropout for regularization, and allows for different activation functions.
+
+    Args:
+        sequence_length: The length of the sequences to be transformed.
+        input_channels: The number of input channels to the module.
+        output_channels: The number of output channels from the module.
+        ff_dim: The dimension of the feed-forward network internal to the module.
+        activation_fn: The activation function used within the feed-forward network.
+        dropout_rate: The dropout probability used for regularization.
+        normalize_before: A boolean indicating whether to apply normalization before
+            the rest of the operations.
+    """
+
+    def __init__(
+        self,
+        sequence_length: int,
+        input_channels: int,
+        output_channels: int,
+        ff_dim: int,
+        activation_fn: Callable[[torch.Tensor], torch.Tensor] = F.relu,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = True,
+        norm_type: type[nn.Module] = TimeBatchNorm2d,
+    ):
+        """Initializes the FeatureMixing module with the provided parameters."""
+        super().__init__()
+
+        self.norm_before = (
+            norm_type((sequence_length, input_channels))
+            if normalize_before
+            else nn.Identity()
+        )
+        self.norm_after = (
+            norm_type((sequence_length, output_channels))
+            if not normalize_before
+            else nn.Identity()
+        )
+
+        self.activation_fn = activation_fn
+        self.dropout = nn.Dropout(dropout_rate)
+        self.fc1 = nn.Linear(input_channels, ff_dim)
+        self.fc2 = nn.Linear(ff_dim, output_channels)
+
+        self.projection = (
+            nn.Linear(input_channels, output_channels)
+            if input_channels != output_channels
+            else nn.Identity()
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass for the FeatureMixing module.
+
+        Args:
+            x: A 3D tensor with shape (N, C, L) where C is the channel dimension.
+
+        Returns:
+            The output tensor after feature mixing.
+        """
+        x_proj = self.projection(x)
+
+        x = self.norm_before(x)
+
+        x = self.fc1(x)  # Apply the first linear transformation.
+        x = self.activation_fn(x)  # Apply the activation function.
+        x = self.dropout(x)  # Apply dropout for regularization.
+        x = self.fc2(x)  # Apply the second linear transformation.
+        x = self.dropout(x)  # Apply dropout again if needed.
+
+        x = x_proj + x  # Add the projection shortcut to the transformed features.
+
+        return self.norm_after(x)
+
+
+class ConditionalFeatureMixing(nn.Module):
+    """Conditional feature mixing module that incorporates static features.
+
+    This module extends the feature mixing process by including static features. It uses
+    a linear transformation to integrate static features into the dynamic feature space,
+    then applies the feature mixing on the concatenated features.
+
+    Args:
+        input_channels: The number of input channels of the dynamic features.
+        output_channels: The number of output channels after feature mixing.
+        static_channels: The number of channels in the static feature input.
+        ff_dim: The inner dimension of the feedforward network used in feature mixing.
+        activation_fn: The activation function used in feature mixing.
+        dropout_rate: The dropout probability used in the feature mixing operation.
+    """
+
+    def __init__(
+        self,
+        sequence_length: int,
+        input_channels: int,
+        output_channels: int,
+        static_channels: int,
+        ff_dim: int,
+        activation_fn: Callable = F.relu,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = False,
+        norm_type: type[nn.Module] = nn.LayerNorm,
+    ):
+        super().__init__()
+
+        self.fr_static = nn.Linear(static_channels, output_channels)
+        self.fm = FeatureMixing(
+            sequence_length,
+            input_channels + output_channels,
+            output_channels,
+            ff_dim,
+            activation_fn,
+            dropout_rate,
+            normalize_before=normalize_before,
+            norm_type=norm_type,
+        )
+
+    def forward(
+        self, x: torch.Tensor, x_static: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Applies conditional feature mixing using both dynamic and static inputs.
+
+        Args:
+            x: A tensor representing dynamic features, typically with shape
+               [batch_size, time_steps, input_channels].
+            x_static: A tensor representing static features, typically with shape
+               [batch_size, static_channels].
+
+        Returns:
+            A tuple containing:
+            - The output tensor after applying conditional feature mixing.
+            - The transformed static features tensor for monitoring or further processing.
+        """
+        v = self.fr_static(x_static)  # Transform static features to match output channels.
+        v = v.unsqueeze(1).repeat(
+            1, x.shape[1], 1
+        )  # Repeat static features across time steps.
+
+        return (
+            self.fm(
+                torch.cat([x, v], dim=-1)
+            ),  # Apply feature mixing on concatenated features.
+            v.detach(),  # Return detached static feature for monitoring or further use.
+        )
+
+
+class TimeMixing(nn.Module):
+    """Applies a transformation over the time dimension of a sequence.
+
+    This module applies a linear transformation followed by an activation function
+    and dropout over the sequence length of the input feature tensor after converting
+    feature maps to the time dimension and then back.
+
+    Args:
+        input_channels: The number of input channels to the module.
+        sequence_length: The length of the sequences to be transformed.
+        activation_fn: The activation function to be used after the linear transformation.
+        dropout_rate: The dropout probability to be used after the activation function.
+    """
+
+    def __init__(
+        self,
+        sequence_length: int,
+        input_channels: int,
+        activation_fn: Callable = F.relu,
+        dropout_rate: float = 0.1,
+        norm_type: type[nn.Module] = TimeBatchNorm2d,
+    ):
+        """Initializes the TimeMixing module with the specified parameters."""
+        super().__init__()
+        self.norm = norm_type((sequence_length, input_channels))
+        self.activation_fn = activation_fn
+        self.dropout = nn.Dropout(dropout_rate)
+        self.fc1 = nn.Linear(sequence_length, sequence_length)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Applies the time mixing operations on the input tensor.
+
+        Args:
+            x: A 3D tensor with shape (N, C, L), where C = channel dimension and
+                L = sequence length.
+
+        Returns:
+            The normalized output tensor after time mixing transformations.
+        """
+        x_temp = feature_to_time(
+            x
+        )  # Convert feature maps to time dimension. Assumes definition elsewhere.
+        x_temp = self.activation_fn(self.fc1(x_temp))
+        x_temp = self.dropout(x_temp)
+        x_res = time_to_feature(x_temp)  # Convert back from time to feature maps.
+
+        return self.norm(x + x_res)  # Apply normalization and combine with original input.
+
+
+class MixerLayer(nn.Module):
+    """A residual block that combines time and feature mixing for sequence data.
+
+    This module sequentially applies time mixing and feature mixing, which are forms
+    of data augmentation and feature transformation that can help in learning temporal
+    dependencies and feature interactions respectively.
+
+    Args:
+        sequence_length: The length of the input sequences.
+        input_channels: The number of input channels to the module.
+        output_channels: The number of output channels from the module.
+        ff_dim: The inner dimension of the feedforward network used in feature mixing.
+        activation_fn: The activation function used in both time and feature mixing.
+        dropout_rate: The dropout probability used in both mixing operations.
+    """
+
+    def __init__(
+        self,
+        sequence_length: int,
+        input_channels: int,
+        output_channels: int,
+        ff_dim: int,
+        activation_fn: Callable = F.relu,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = False,
+        norm_type: type[nn.Module] = nn.LayerNorm,
+    ):
+        """Initializes the MixLayer with time and feature mixing modules."""
+        super().__init__()
+
+        self.time_mixing = TimeMixing(
+            sequence_length,
+            input_channels,
+            activation_fn,
+            dropout_rate,
+            norm_type=norm_type,
+        )
+        self.feature_mixing = FeatureMixing(
+            sequence_length,
+            input_channels,
+            output_channels,
+            ff_dim,
+            activation_fn,
+            dropout_rate,
+            norm_type=norm_type,
+            normalize_before=normalize_before,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass for the MixLayer module.
+
+        Args:
+            x: A 3D tensor with shape (N, C, L) to be processed by the mixing layers.
+
+        Returns:
+            The output tensor after applying time and feature mixing operations.
+        """
+        x = self.time_mixing(x)  # Apply time mixing first.
+        x = self.feature_mixing(x)  # Then apply feature mixing.
+
+        return x
+
+
+class ConditionalMixerLayer(nn.Module):
+    """Conditional mix layer combining time and feature mixing with static context.
+
+    This module combines time mixing and conditional feature mixing, where the latter
+    is influenced by static features. This allows the module to learn representations
+    that are influenced by both dynamic and static features.
+
+    Args:
+        sequence_length: The length of the input sequences.
+        input_channels: The number of input channels of the dynamic features.
+        output_channels: The number of output channels after feature mixing.
+        static_channels: The number of channels in the static feature input.
+        ff_dim: The inner dimension of the feedforward network used in feature mixing.
+        activation_fn: The activation function used in both mixing operations.
+        dropout_rate: The dropout probability used in both mixing operations.
+    """
+
+    def __init__(
+        self,
+        sequence_length: int,
+        input_channels: int,
+        output_channels: int,
+        static_channels: int,
+        ff_dim: int,
+        activation_fn: Callable = F.relu,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = False,
+        norm_type: type[nn.Module] = nn.LayerNorm,
+    ):
+        super().__init__()
+
+        self.time_mixing = TimeMixing(
+            sequence_length,
+            input_channels,
+            activation_fn,
+            dropout_rate,
+            norm_type=norm_type,
+        )
+        self.feature_mixing = ConditionalFeatureMixing(
+            sequence_length,
+            input_channels,
+            output_channels=output_channels,
+            static_channels=static_channels,
+            ff_dim=ff_dim,
+            activation_fn=activation_fn,
+            dropout_rate=dropout_rate,
+            normalize_before=normalize_before,
+            norm_type=norm_type,
+        )
+
+    def forward(self, x: torch.Tensor, x_static: torch.Tensor) -> torch.Tensor:
+        """Forward pass for the conditional mix layer.
+
+        Args:
+            x: A tensor representing dynamic features, typically with shape
+               [batch_size, time_steps, input_channels].
+            x_static: A tensor representing static features, typically with shape
+               [batch_size, static_channels].
+
+        Returns:
+            The output tensor after applying time and conditional feature mixing.
+        """
+        x = self.time_mixing(x)  # Apply time mixing first.
+        x, _ = self.feature_mixing(x, x_static)  # Then apply conditional feature mixing.
+
+        return x
+
+
+def time_to_feature(x: torch.Tensor) -> torch.Tensor:
+    """Converts a time series tensor to a feature tensor."""
+    return x.permute(0, 2, 1)
+
+
+feature_to_time = time_to_feature
\ No newline at end of file
diff --git a/probts/model/nn/layers/__init__.py b/probts/model/nn/layers/__init__.py
index 4a63681..681e170 100644
--- a/probts/model/nn/layers/__init__.py
+++ b/probts/model/nn/layers/__init__.py
@@ -1,5 +1,5 @@
-from .PatchTST_backbone import *
-from .PatchTST_layers import *
+from .PatchTSTModule.PatchTST_backbone import *
+from .PatchTSTModule.PatchTST_layers import *
 from .RevIN import *
-from .diffusion_layers import *
+# from .diffusion_layers import *
 # from .Moirai_backbone import *
\ No newline at end of file
diff --git a/probts/model/nn/layers/diffusion_layers.py b/probts/model/nn/layers/diffusion_layers.py
deleted file mode 100644
index 82eb090..0000000
--- a/probts/model/nn/layers/diffusion_layers.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# ---------------------------------------------------------------------------------
-# Portions of this file are derived from PyTorch-TS
-# - Source: https://github.com/zalandoresearch/pytorch-ts
-# - Paper: Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
-# - License: MIT, Apache-2.0 license
-
-# We thank the authors for their contributions.
-# ---------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from linear_attention_transformer import LinearAttentionTransformer
-
-def get_torch_trans(heads=8, layers=1, channels=64,linear=False):
-    if linear:
-        encoder_layer = LinearAttentionTransformer(
-            dim = channels,
-            heads = heads,
-            depth = layers,
-            max_seq_len = 4096,
-            n_local_attn_heads = 0
-        )
-        return encoder_layer
-    else:
-        encoder_layer = nn.TransformerEncoderLayer(
-            d_model=channels, nhead=heads, dim_feedforward=64, activation="gelu"
-        )
-        return nn.TransformerEncoder(encoder_layer, num_layers=layers)
-
-
-def Conv1d_with_init(in_channels, out_channels, kernel_size):
-    layer = nn.Conv1d(in_channels, out_channels, kernel_size)
-    nn.init.kaiming_normal_(layer.weight)
-    return layer
-
-
-class DiffusionEmbedding(nn.Module):
-    def __init__(self, dim=128, proj_dim=None, max_steps=500):
-        super().__init__()
-        if proj_dim is None:
-            proj_dim = dim
-        self.register_buffer(
-            "embedding", self._build_embedding(dim, max_steps), persistent=False
-        )
-        self.projection1 = nn.Linear(dim * 2, proj_dim)
-        self.projection2 = nn.Linear(proj_dim, proj_dim)
-
-    def forward(self, diffusion_step):
-        x = self.embedding[diffusion_step]
-        x = self.projection1(x)
-        x = F.silu(x)
-        x = self.projection2(x)
-        x = F.silu(x)
-        return x
-
-    def _build_embedding(self, dim, max_steps):
-        steps = torch.arange(max_steps).unsqueeze(1)  # [T,1]
-        dims = torch.arange(dim).unsqueeze(0)  # [1,dim]
-        table = steps * 10.0 ** (dims * 4.0 / dim)  # [T,dim]
-        table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
-        return table
-
-
-class diff_CSDI(nn.Module):
-    def __init__(self, channels, diffusion_embedding_dim, side_dim, num_steps, nheads, n_layers, inputdim=2, linear=False):
-        super().__init__()
-        self.channels = channels
-
-        self.diffusion_embedding = DiffusionEmbedding(
-            dim=diffusion_embedding_dim, max_steps=num_steps
-        )
-        self.input_projection = Conv1d_with_init(inputdim, self.channels, 1)
-        self.output_projection1 = Conv1d_with_init(self.channels, self.channels, 1)
-        self.output_projection2 = Conv1d_with_init(self.channels, 1, 1)
-        nn.init.zeros_(self.output_projection2.weight)
-
-        self.residual_layers = nn.ModuleList(
-            [
-                ResidualBlock(
-                    side_dim=side_dim,
-                    channels=self.channels,
-                    diffusion_embedding_dim=diffusion_embedding_dim,
-                    nheads=nheads,
-                    linear=linear,
-                )
-                for _ in range(n_layers)
-            ]
-        )
-
-    def forward(self, x, cond_info, diffusion_step):
-        B, inputdim, K, L = x.shape
-
-        x = x.reshape(B, inputdim, K * L)
-
-        x = self.input_projection(x)
-        x = F.relu(x)
-        x = x.reshape(B, self.channels, K, L)
-
-        diffusion_emb = self.diffusion_embedding(diffusion_step)
-
-        skip = []
-        for layer in self.residual_layers:
-            x, skip_connection = layer(x, cond_info, diffusion_emb)
-            skip.append(skip_connection)
-
-        x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers))
-        x = x.reshape(B, self.channels, K * L)
-        x = self.output_projection1(x)  # (B,channel,K*L)
-        x = F.relu(x)
-        x = self.output_projection2(x)  # (B,1,K*L)
-        x = x.reshape(B, K, L)
-        return x
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, side_dim, channels, diffusion_embedding_dim, nheads, linear=False):
-        super().__init__()
-        self.side_dim = side_dim
-        self.diffusion_projection = nn.Linear(diffusion_embedding_dim, channels)
-        self.cond_projection = Conv1d_with_init(side_dim, 2 * channels, 1)
-        self.mid_projection = Conv1d_with_init(channels, 2 * channels, 1)
-        self.output_projection = Conv1d_with_init(channels, 2 * channels, 1)
-
-        self.time_layer = get_torch_trans(heads=nheads, layers=1, channels=channels,linear=linear)
-        self.feature_layer = get_torch_trans(heads=nheads, layers=1, channels=channels,linear=linear)
-
-    def forward_time(self, y, base_shape):
-        B, channel, K, L = base_shape
-        if L == 1:
-            return y
-        y = y.reshape(B, channel, K, L).permute(0, 2, 1, 3).reshape(B * K, channel, L)
-        y = self.time_layer(y.permute(2, 0, 1)).permute(1, 2, 0)
-        y = y.reshape(B, K, channel, L).permute(0, 2, 1, 3).reshape(B, channel, K * L)
-        return y
-
-    def forward_feature(self, y, base_shape):
-        B, channel, K, L = base_shape
-        if K == 1:
-            return y
-        y = y.reshape(B, channel, K, L).permute(0, 3, 1, 2).reshape(B * L, channel, K)
-        y = self.feature_layer(y.permute(2, 0, 1)).permute(1, 2, 0)
-        y = y.reshape(B, L, channel, K).permute(0, 2, 3, 1).reshape(B, channel, K * L)
-        return y
-
-    def forward(self, x, cond_info, diffusion_emb):
-
-        B, channel, K, L = x.shape
-        base_shape = x.shape
-        x = x.reshape(B, channel, K * L)
-
-        diffusion_emb = self.diffusion_projection(diffusion_emb).unsqueeze(-1)  # (B,channel,1)
-        y = x + diffusion_emb
-
-        y = self.forward_time(y, base_shape)
-        y = self.forward_feature(y, base_shape)  # (B,channel,K*L)
-        y = self.mid_projection(y)  # (B,2*channel,K*L)
-        _, cond_dim, _, _ = cond_info.shape
-        cond_info = cond_info.reshape(B, cond_dim, K * L)
-        cond_info = self.cond_projection(cond_info)  # (B,2*channel,K*L)
-        y = y + cond_info
-
-        gate, filter = torch.chunk(y, 2, dim=1)
-        y = torch.sigmoid(gate) * torch.tanh(filter)  # (B,channel,K*L)
-        y = self.output_projection(y)
-
-        residual, skip = torch.chunk(y, 2, dim=1)
-        x = x.reshape(base_shape)
-        residual = residual.reshape(base_shape)
-        skip = skip.reshape(base_shape)
-        return (x + residual) / math.sqrt(2.0), skip
diff --git a/probts/model/nn/layers/s4.py b/probts/model/nn/layers/s4.py
deleted file mode 100644
index 01fb14a..0000000
--- a/probts/model/nn/layers/s4.py
+++ /dev/null
@@ -1,1840 +0,0 @@
-"""Standalone version of Structured (Sequence) State Space (S4) model."""
-
-import logging
-from functools import partial
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from pytorch_lightning.utilities import rank_zero_only
-from einops import rearrange, repeat
-import opt_einsum as oe
-
-contract = oe.contract
-contract_expression = oe.contract_expression
-
-
-def get_logger(name=__name__, level=logging.INFO) -> logging.Logger:
-    """Initializes multi-GPU-friendly python logger."""
-
-    logger = logging.getLogger(name)
-    logger.setLevel(level)
-
-    # this ensures all logging levels get marked with the rank zero decorator
-    # otherwise logs would get multiplied for each GPU process in multi-GPU setup
-    for level in (
-        "debug",
-        "info",
-        "warning",
-        "error",
-        "exception",
-        "fatal",
-        "critical",
-    ):
-        setattr(logger, level, rank_zero_only(getattr(logger, level)))
-
-    return logger
-
-
-log = get_logger(__name__)
-
-""" Cauchy and Vandermonde kernels """
-
-try:  # Try CUDA extension
-    from extensions.cauchy.cauchy import cauchy_mult
-
-    has_cauchy_extension = True
-except ImportError:
-    # log.warning(
-    #     "CUDA extension for cauchy multiplication not found. Install by going to extensions/cauchy/ and running `python setup.py install`. This should speed up end-to-end training by 10-50%"
-    # )
-    has_cauchy_extension = False
-
-try:  # Try pykeops
-    from pykeops.torch import Genred
-
-    has_pykeops = True
-    # log.info("Pykeops installation found.")
-
-    def _broadcast_dims(*tensors):
-        max_dim = max([len(tensor.shape) for tensor in tensors])
-        tensors = [
-            tensor.view((1,) * (max_dim - len(tensor.shape)) + tensor.shape)
-            for tensor in tensors
-        ]
-        return tensors
-
-    def cauchy_conj(v, z, w):
-        """Pykeops version"""
-        expr_num = "z * ComplexReal(v) - Real2Complex(Sum(v * w))"
-        expr_denom = "ComplexMult(z-w, z-Conj(w))"
-
-        cauchy_mult = Genred(
-            f"ComplexDivide({expr_num}, {expr_denom})",
-            [
-                "v = Vj(2)",
-                "z = Vi(2)",
-                "w = Vj(2)",
-            ],
-            reduction_op="Sum",
-            axis=1,
-        )
-
-        v, z, w = _broadcast_dims(v, z, w)
-        v = _c2r(v)
-        z = _c2r(z)
-        w = _c2r(w)
-
-        r = 2 * cauchy_mult(v, z, w, backend="GPU")
-        return _r2c(r)
-
-    def log_vandermonde(v, x, L):
-        expr = "ComplexMult(v, ComplexExp(ComplexMult(x, l)))"
-        vandermonde_mult = Genred(
-            expr,
-            [
-                "v = Vj(2)",
-                "x = Vj(2)",
-                "l = Vi(2)",
-            ],
-            reduction_op="Sum",
-            axis=1,
-        )
-
-        l = torch.arange(L).to(x)
-        v, x, l = _broadcast_dims(v, x, l)
-        v = _c2r(v)
-        x = _c2r(x)
-        l = _c2r(l)
-
-        r = vandermonde_mult(v, x, l, backend="GPU")
-        return 2 * _r2c(r).real
-
-    def log_vandermonde_transpose(u, v, x, L):
-        """
-        u: ... H L
-        v: ... H N
-        x: ... H N
-        Returns: ... H N
-
-        V = Vandermonde(a, L) : (H N L)
-        contract_L(V * u * v)
-        """
-        expr = "ComplexMult(ComplexMult(v, u), ComplexExp(ComplexMult(x, l)))"
-        vandermonde_mult = Genred(
-            expr,
-            [
-                "u = Vj(2)",
-                "v = Vi(2)",
-                "x = Vi(2)",
-                "l = Vj(2)",
-            ],
-            reduction_op="Sum",
-            axis=1,
-        )
-
-        l = torch.arange(L).to(x)
-        u, v, x, l = _broadcast_dims(u, v, x, l)
-        u = _c2r(u)
-        v = _c2r(v)
-        x = _c2r(x)
-        l = _c2r(l)
-
-        r = vandermonde_mult(u, v, x, l, backend="GPU")
-        return _r2c(r)
-
-except ImportError:
-    has_pykeops = False
-    if not has_cauchy_extension:
-        # log.warning(
-        #     "Falling back on slow Cauchy kernel. Install at least one of pykeops or the CUDA extension for efficiency."
-        # )
-
-        def cauchy_naive(v, z, w):
-            """
-            v, w: (..., N)
-            z: (..., L)
-            returns: (..., L)
-            """
-            cauchy_matrix = v.unsqueeze(-1) / (
-                z.unsqueeze(-2) - w.unsqueeze(-1)
-            )  # (... N L)
-            return torch.sum(cauchy_matrix, dim=-2)
-
-    # Vandermonde functions
-    # log.warning(
-    #     "Falling back on slow Vandermonde kernel. Install pykeops for improved memory efficiency."
-    # )
-
-    def log_vandermonde(v, x, L):
-        """
-        v: (..., N)
-        x: (..., N)
-        returns: (..., L) \sum v x^l
-        """
-        vandermonde_matrix = torch.exp(
-            x.unsqueeze(-1) * torch.arange(L).to(x)
-        )  # (... N L)
-        vandermonde_prod = contract(
-            "... n, ... n l -> ... l", v, vandermonde_matrix
-        )  # (... L)
-        return 2 * vandermonde_prod.real
-
-    def log_vandermonde_transpose(u, v, x, L):
-        vandermonde_matrix = torch.exp(
-            x.unsqueeze(-1) * torch.arange(L).to(x)
-        )  # (... N L)
-        vandermonde_prod = contract(
-            "... l, ... n, ... n l -> ... n",
-            u.to(x),
-            v.to(x),
-            vandermonde_matrix,
-        )  # (... L)
-        return vandermonde_prod
-
-
-def _conj(x):
-    return torch.cat([x, x.conj()], dim=-1)
-
-
-_c2r = torch.view_as_real
-_r2c = torch.view_as_complex
-if tuple(map(int, torch.__version__.split(".")[:2])) >= (1, 10):
-
-    def _resolve_conj(x):
-        return x.conj().resolve_conj()
-
-else:
-
-    def _resolve_conj(x):
-        return x.conj()
-
-
-""" Simple nn.Module components """
-
-
-def Activation(activation=None, dim=-1):
-    if activation in [None, "id", "identity", "linear"]:
-        return nn.Identity()
-    elif activation == "tanh":
-        return nn.Tanh()
-    elif activation == "relu":
-        return nn.ReLU()
-    elif activation == "gelu":
-        return nn.GELU()
-    elif activation in ["swish", "silu"]:
-        return nn.SiLU()
-    elif activation == "glu":
-        return nn.GLU(dim=dim)
-    elif activation == "sigmoid":
-        return nn.Sigmoid()
-    else:
-        raise NotImplementedError(
-            "hidden activation '{}' is not implemented".format(activation)
-        )
-
-
-def LinearActivation(
-    d_input,
-    d_output,
-    bias=True,
-    transposed=False,
-    activation=None,
-    activate=False,  # Apply activation as part of this module
-    **kwargs,
-):
-    """Returns a linear nn.Module with control over axes order, initialization, and activation"""
-
-    # Construct core module
-    linear_cls = partial(nn.Conv1d, kernel_size=1) if transposed else nn.Linear
-    if activation == "glu":
-        d_output *= 2
-    linear = linear_cls(d_input, d_output, bias=bias, **kwargs)
-
-    if activate and activation is not None:
-        activation = Activation(activation, dim=-2 if transposed else -1)
-        linear = nn.Sequential(linear, activation)
-    return linear
-
-
-class DropoutNd(nn.Module):
-    def __init__(self, p: float = 0.5, tie=True, transposed=True):
-        """
-        tie: tie dropout mask across sequence lengths (Dropout1d/2d/3d)
-        """
-        super().__init__()
-        if p < 0 or p >= 1:
-            raise ValueError(
-                "dropout probability has to be in [0, 1), "
-                "but got {}".format(p)
-            )
-        self.p = p
-        self.tie = tie
-        self.transposed = transposed
-        self.binomial = torch.distributions.binomial.Binomial(probs=1 - self.p)
-
-    def forward(self, X):
-        """X: (batch, dim, lengths...)"""
-        if self.training:
-            if not self.transposed:
-                X = rearrange(X, "b d ... -> b ... d")
-            mask_shape = (
-                X.shape[:2] + (1,) * (X.ndim - 2) if self.tie else X.shape
-            )
-            mask = torch.rand(*mask_shape, device=X.device) < 1.0 - self.p
-            X = X * mask * (1.0 / (1 - self.p))
-            if not self.transposed:
-                X = rearrange(X, "b ... d -> b d ...")
-            return X
-        return X
-
-
-""" Misc functional utilities """
-
-
-def power(L, A, v=None):
-    """Compute A^L and the scan sum_i A^i v_i
-
-    A: (..., N, N)
-    v: (..., N, L)
-    """
-
-    I = torch.eye(A.shape[-1]).to(A)  # , dtype=A.dtype, device=A.device)
-
-    powers = [A]
-    l = 1
-    while True:
-        if L % 2 == 1:
-            I = powers[-1] @ I
-        L //= 2
-        if L == 0:
-            break
-        l *= 2
-        powers.append(powers[-1] @ powers[-1])
-
-    if v is None:
-        return I
-
-    # Invariants:
-    # powers[-1] := A^l
-    # l := largest po2 at most L
-
-    # Note that an alternative divide and conquer to compute the reduction is possible and can be embedded into the above loop without caching intermediate powers of A
-    # We do this reverse divide-and-conquer for efficiency reasons:
-    # 1) it involves fewer padding steps for non-po2 L
-    # 2) it involves more contiguous arrays
-
-    # Take care of edge case for non-po2 arrays
-    # Note that this initial step is a no-op for the case of power of 2 (l == L)
-    k = v.size(-1) - l
-    v_ = powers.pop() @ v[..., l:]
-    v = v[..., :l]
-    v[..., :k] = v[..., :k] + v_
-
-    # Handle reduction for power of 2
-    while v.size(-1) > 1:
-        v = rearrange(v, "... (z l) -> ... z l", z=2)
-        v = v[..., 0, :] + powers.pop() @ v[..., 1, :]
-    return I, v.squeeze(-1)
-
-
-""" HiPPO utilities """
-
-
-def transition(measure, N):
-    """A, B transition matrices for different measures"""
-    # Legendre (translated)
-    if measure == "legt":
-        Q = np.arange(N, dtype=np.float64)
-        R = (2 * Q + 1) ** 0.5
-        j, i = np.meshgrid(Q, Q)
-        A = R[:, None] * np.where(i < j, (-1.0) ** (i - j), 1) * R[None, :]
-        B = R[:, None]
-        A = -A
-
-        # Halve again for timescale correctness
-        A *= 0.5
-        B *= 0.5
-    # Legendre (scaled)
-    elif measure == "legs":
-        q = np.arange(N, dtype=np.float64)
-        col, row = np.meshgrid(q, q)
-        r = 2 * q + 1
-        M = -(np.where(row >= col, r, 0) - np.diag(q))
-        T = np.sqrt(np.diag(2 * q + 1))
-        A = T @ M @ np.linalg.inv(T)
-        B = np.diag(T)[:, None]
-        B = (
-            B.copy()
-        )  # Otherwise "UserWarning: given NumPY array is not writeable..." after torch.as_tensor(B)
-    elif measure == "legsd":
-        # Essentially equivalent to S4D-LegS
-        q = np.arange(N, dtype=np.float64)
-        col, row = np.meshgrid(q, q)
-        r = 2 * q + 1
-        M = -(np.where(row >= col, r, 0) - np.diag(q))
-        T = np.sqrt(np.diag(2 * q + 1))
-        A = T @ M @ np.linalg.inv(T)
-        B = np.diag(T)[:, None]
-        B = (
-            B.copy()
-        )  # Otherwise "UserWarning: given NumPY array is not writeable..." after torch.as_tensor(B)
-        A += 0.5 * B * B[None, :, 0]
-        B = B / 2.0
-    elif measure in ["fourier_diag", "foud"]:
-        # Essentially equivalent to S4D-Lin
-        freqs = np.arange(N // 2)
-        d = np.stack([freqs, np.zeros(N // 2)], axis=-1).reshape(-1)[:-1]
-        A = 2 * np.pi * (-np.diag(d, 1) + np.diag(d, -1))
-        A = A - 0.5 * np.eye(N)
-        B = np.zeros(N)
-        B[0::2] = 2**0.5
-        B[0] = 1
-        B = B[:, None]
-    elif measure in ["fourier", "fout"]:
-        freqs = np.arange(N // 2)
-        d = np.stack([np.zeros(N // 2), freqs], axis=-1).reshape(-1)[1:]
-        A = np.pi * (-np.diag(d, 1) + np.diag(d, -1))
-        B = np.zeros(N)
-        B[0::2] = 2**0.5
-        B[0] = 1
-
-        # Subtract off rank correction - this corresponds to the other endpoint u(t-1) in this case
-        A = A - B[:, None] * B[None, :]
-        B = B[:, None]
-    else:
-        raise NotImplementedError
-
-    return A, B
-
-
-def rank_correction(measure, N, rank=1, dtype=torch.float):
-    """Return low-rank matrix L such that A + L is normal"""
-
-    if measure == "legs":
-        assert rank >= 1
-        P = torch.sqrt(0.5 + torch.arange(N, dtype=dtype)).unsqueeze(
-            0
-        )  # (1 N)
-    elif measure == "legt":
-        assert rank >= 2
-        P = torch.sqrt(1 + 2 * torch.arange(N, dtype=dtype))  # (N)
-        P0 = P.clone()
-        P0[0::2] = 0.0
-        P1 = P.clone()
-        P1[1::2] = 0.0
-        P = torch.stack([P0, P1], dim=0)  # (2 N)
-        P *= 2 ** (
-            -0.5
-        )  # Halve the rank correct just like the original matrix was halved
-    elif measure in ["fourier", "fout"]:
-        P = torch.zeros(N)
-        P[0::2] = 2**0.5
-        P[0] = 1
-        P = P.unsqueeze(0)
-    elif measure in ["fourier_diag", "foud", "legsd"]:
-        P = torch.zeros(1, N, dtype=dtype)
-    else:
-        raise NotImplementedError
-
-    d = P.size(0)
-    if rank > d:
-        P = torch.cat(
-            [P, torch.zeros(rank - d, N, dtype=dtype)], dim=0
-        )  # (rank N)
-    return P
-
-
-def nplr(measure, N, rank=1, dtype=torch.float, diagonalize_precision=True):
-    """Return w, p, q, V, B such that
-    (w - p q^*, B) is unitarily equivalent to the original HiPPO A, B by the matrix V
-    i.e. A = V[w - p q^*]V^*, B = V B
-    """
-    assert dtype == torch.float or dtype == torch.double
-    cdtype = torch.cfloat if dtype == torch.float else torch.cdouble
-
-    A, B = transition(measure, N)
-    A = torch.as_tensor(A, dtype=dtype)  # (N, N)
-    B = torch.as_tensor(B, dtype=dtype)[:, 0]  # (N,)
-
-    P = rank_correction(measure, N, rank=rank, dtype=dtype)  # (r N)
-    AP = A + torch.sum(P.unsqueeze(-2) * P.unsqueeze(-1), dim=-3)
-
-    # We require AP to be nearly skew-symmetric
-    _A = AP + AP.transpose(-1, -2)
-    if (
-        err := torch.sum((_A - _A[0, 0] * torch.eye(N)) ** 2) / N
-    ) > 1e-5:  # if not torch.allclose(_A - _A[0,0]*torch.eye(N), torch.zeros(N, N), atol=1e-5):
-        print("WARNING: HiPPO matrix not skew symmetric", err)
-
-    # Take advantage of identity + skew-symmetric form to calculate real and imaginary parts separately
-    # Imaginary part can use eigh instead of eig
-    w_re = torch.mean(torch.diagonal(AP), -1, keepdim=True)
-
-    # Diagonalize in double precision
-    if diagonalize_precision:
-        AP = AP.to(torch.double)
-    w_im, V = torch.linalg.eigh(AP * -1j)  # (..., N) (..., N, N)
-    if diagonalize_precision:
-        w_im, V = w_im.to(cdtype), V.to(cdtype)
-    w = w_re + 1j * w_im
-    # Check: V w V^{-1} = A
-    # print("check", V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2))
-
-    # Only keep half of each conjugate pair
-    _, idx = torch.sort(w.imag)
-    w_sorted = w[idx]
-    V_sorted = V[:, idx]
-
-    # There is an edge case when eigenvalues can be 0, which requires some machinery to handle
-    # We use a huge hack here: Assume only one pair is 0, and that it is the first row/column of A (only happens in Fourier case)
-    V = V_sorted[:, : N // 2]
-    w = w_sorted[: N // 2]
-    assert (
-        w[-2].abs() > 1e-4
-    ), "Only 1 zero eigenvalue allowed in diagonal part of A"
-    if w[-1].abs() < 1e-4:
-        V[:, -1] = 0.0
-        V[0, -1] = 2**-0.5
-        V[1, -1] = 2**-0.5 * 1j
-
-    _AP = V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2)
-    if (err := torch.sum((2 * _AP.real - AP) ** 2) / N) > 1e-5:
-        print(
-            "Warning: Diagonalization of A matrix not numerically precise - error",
-            err,
-        )
-    # print("check", V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2))
-
-    V_inv = V.conj().transpose(-1, -2)
-
-    B = contract("ij, j -> i", V_inv, B.to(V))  # V^* B
-    P = contract("ij, ...j -> ...i", V_inv, P.to(V))  # V^* P
-
-    return w, P, B, V
-
-
-def dplr(
-    scaling,
-    N,
-    rank=1,
-    H=1,
-    dtype=torch.float,
-    real_scale=1.0,
-    imag_scale=1.0,
-    random_real=False,
-    random_imag=False,
-    normalize=False,
-    diagonal=True,
-    random_B=False,
-):
-    assert dtype == torch.float or dtype == torch.double
-    dtype = torch.cfloat if dtype == torch.float else torch.cdouble
-
-    pi = torch.tensor(math.pi)
-    if random_real:
-        real_part = torch.rand(H, N // 2)
-    else:
-        real_part = 0.5 * torch.ones(H, N // 2)
-    if random_imag:
-        imag_part = N // 2 * torch.rand(H, N // 2)
-    else:
-        imag_part = repeat(torch.arange(N // 2), "n -> h n", h=H)
-
-    real_part = real_scale * real_part
-    if scaling == "random":
-        imag_part = torch.randn(H, N // 2)
-    elif scaling == "real":
-        imag_part = 0 * imag_part
-        real_part = 1 + repeat(torch.arange(N // 2), "n -> h n", h=H)
-    elif scaling in ["linear", "lin"]:
-        imag_part = pi * imag_part
-    elif scaling in [
-        "inverse",
-        "inv",
-    ]:  # Based on asymptotics of the default HiPPO matrix
-        imag_part = 1 / pi * N * (N / (1 + 2 * imag_part) - 1)
-    elif scaling in ["inverse2", "inv2"]:
-        imag_part = 1 / pi * N * (N / (1 + imag_part) - 1)
-    elif scaling in ["quadratic", "quad"]:
-        imag_part = 1 / pi * (1 + 2 * imag_part) ** 2
-    elif scaling in ["legs", "hippo"]:
-        w, _, _, _ = nplr("legsd", N)
-        imag_part = w.imag
-
-    else:
-        raise NotImplementedError
-    imag_part = imag_scale * imag_part
-    w = -real_part + 1j * imag_part
-
-    # Initialize B
-    if random_B:
-        B = torch.randn(H, N // 2, dtype=dtype)
-    else:
-        B = torch.ones(H, N // 2, dtype=dtype)
-
-    if normalize:
-        norm = (
-            -B / w
-        )  # (H, N) # Result if you integrate the kernel with constant 1 function
-        zeta = 2 * torch.sum(
-            torch.abs(norm) ** 2, dim=-1, keepdim=True
-        )  # Variance with a random C vector
-        B = B / zeta**0.5
-
-    P = torch.randn(rank, H, N // 2, dtype=dtype)
-    if diagonal:
-        P = P * 0.0
-    V = torch.eye(N, dtype=dtype)[:: N // 2]  # Only used in testing
-    V = repeat(V, "n m -> h n m", h=H)
-
-    return w, P, B, V
-
-
-def ssm(measure, N, R, H, **ssm_args):
-    """Dispatcher to create single SSM initialization
-
-    N: state size
-    R: rank (for DPLR parameterization)
-    H: number of independent SSM copies
-    """
-
-    if measure == "dplr":
-        w, P, B, V = dplr(N=N, rank=R, H=H, **ssm_args)
-    elif measure.startswith("diag"):
-        args = measure.split("-")
-        assert args[0] == "diag" and len(args) > 1
-        scaling = args[1]
-        w, P, B, V = dplr(
-            scaling=scaling, N=N, rank=R, H=H, diagonal=True, **ssm_args
-        )
-    else:
-        w, P, B, V = nplr(measure, N, R, **ssm_args)
-        w = repeat(w, "n -> s n", s=H)
-        P = repeat(P, "r n -> r s n", s=H)
-        B = repeat(B, "n -> s n", s=H)
-        V = repeat(V, "n m -> s n m", s=H)
-    return w, P, B, V
-
-
-combinations = {
-    "hippo": ["legs", "fourier"],
-    "diag": ["diag-inv", "diag-lin"],
-    "all": ["legs", "fourier", "diag-inv", "diag-lin"],
-}
-
-
-def combination(measures, N, R, S, **ssm_args):
-    if isinstance(measures, str):
-        measures = (
-            combinations[measures] if measures in combinations else [measures]
-        )
-
-    assert (
-        S % len(measures) == 0
-    ), f"{S} independent trainable SSM copies must be multiple of {len(measures)} different measures"
-    w, P, B, V = zip(
-        *[
-            ssm(measure, N, R, S // len(measures), **ssm_args)
-            for measure in measures
-        ]
-    )
-    w = torch.cat(w, dim=0)  # (S N)
-    P = torch.cat(P, dim=1)  # (R S N)
-    B = torch.cat(B, dim=0)  # (S N)
-    V = torch.cat(V, dim=0)  # (S N N)
-    return w, P, B, V
-
-
-class OptimModule(nn.Module):
-    """Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters"""
-
-    def register(self, name, tensor, lr=None):
-        """Register a tensor with a configurable learning rate and 0 weight decay"""
-
-        if lr == 0.0:
-            self.register_buffer(name, tensor)
-        else:
-            self.register_parameter(name, nn.Parameter(tensor))
-
-            optim = {"weight_decay": 0.0}
-            if lr is not None:
-                optim["lr"] = lr
-            setattr(getattr(self, name), "_optim", optim)
-
-
-class SSKernelNPLR(OptimModule):
-    """Stores a representation of and computes the SSKernel function K_L(A^dt, B^dt, C) corresponding to a discretized state space, where A is Normal + Low Rank (NPLR)"""
-
-    @torch.no_grad()
-    def _setup_C(self, L):
-        """Construct C~ from C
-
-        Two modes are supported: go directly to length L if self.L is 1, or length is doubled
-        """
-
-        if self.L.item() == 0:
-            if self.verbose:
-                log.info(f"S4: Initializing kernel to length {L}")
-            double_length = False
-        elif L > self.L.item():  # 2*int(self.L) == L:
-            if self.verbose:
-                log.info(
-                    f"S4: Doubling length from L = {self.L.item()} to {2*self.L.item()}"
-                )
-            double_length = True
-            L = self.L.item()  # Convenience for the math below
-        else:
-            return
-
-        C = _r2c(self.C)
-        dA, _ = self._setup_state()
-        dA_L = power(L, dA)
-        # Multiply C by I - dA_L
-        C_ = _conj(C)
-        prod = contract("h m n, c h n -> c h m", dA_L.transpose(-1, -2), C_)
-        if double_length:
-            prod = -prod  # Multiply by I + dA_L instead
-        C_ = C_ - prod
-        C_ = C_[..., : self.N]  # Take conjugate pairs again
-        self.C.copy_(_c2r(C_))
-
-        self.L = (
-            2 * self.L if double_length else self.L + L
-        )  # Preserve type/device
-
-    def _omega(self, L, dtype, device, cache=True):
-        """Calculate (and cache) FFT nodes and their "unprocessed" version with the bilinear transform
-        This should be called everytime the internal length self.L changes"""
-
-        # Use cached if available
-        if (
-            cache
-            and hasattr(self, "omega")
-            and self.omega.size(-1) == L // 2 + 1
-        ):
-            return self.omega, self.z
-
-        omega = torch.tensor(
-            np.exp(-2j * np.pi / (L)), dtype=dtype, device=device
-        )  # \omega_{2L}
-        omega = omega ** torch.arange(0, L // 2 + 1, device=device)
-        z = 2 * (1 - omega) / (1 + omega)
-
-        # Cache if necessary
-        if cache:
-            self.omega = omega
-            self.z = z
-        return omega, z
-
-    def __init__(
-        self,
-        w,
-        P,
-        B,
-        C,
-        log_dt,
-        L=None,  # starting/maximum length of kernel
-        lr=None,
-        verbose=False,
-        keops=False,
-        real_type="exp",  # ['none' | 'exp' | 'relu' | sigmoid']
-        real_tolerance=1e-3,
-        bandlimit=None,
-    ):
-        """
-        L: Maximum length; this module computes an SSM kernel of length L
-        A is represented by diag(w) - PP^*
-        w: (S, N) diagonal part
-        P: (R, S, N) low-rank part
-
-        B: (S, N)
-        C: (C, H, N)
-        dt: (H) timescale per feature
-        lr: [dict | float | None] hook to set lr of special parameters (A, B, dt)
-
-        Dimensions:
-        N (or d_state): state size
-        H (or d_model): total SSM copies
-        S (or n_ssm): number of trainable copies of (A, B, dt); must divide H
-        R (or rank): rank of low-rank part
-        C (or channels): system is 1-dim to C-dim
-
-        The forward pass of this Module returns a tensor of shape (C, H, L)
-
-        Note: tensor shape N here denotes half the true state size, because of conjugate symmetry
-        """
-
-        super().__init__()
-        self.verbose = verbose
-        self.keops = keops
-        self.bandlimit = bandlimit
-        self.real_type = real_type
-        self.real_tolerance = real_tolerance
-
-        # Rank of low-rank correction
-        self.rank = P.shape[-3]
-        assert w.size(-1) == P.size(-1) == B.size(-1) == C.size(-1)
-        self.H = log_dt.size(-1)
-        self.N = w.size(-1)
-
-        # Check different SSM inits
-        assert w.size(-2) == P.size(-2) == B.size(-2)  # n_ssm
-        assert self.H % w.size(0) == 0
-        self.n_ssm = w.size(0)
-        self.repeat = self.H // w.size(
-            0
-        )  # Each trainable SSM needs to be duplicated this many times
-
-        # Broadcast everything to correct shapes
-        C = C.expand(
-            torch.broadcast_shapes(C.shape, (1, self.H, self.N))
-        )  # (C, H, N)
-        B = B.unsqueeze(0)  # (1, 1, N)
-
-        # Register parameters
-        self.C = nn.Parameter(_c2r(_resolve_conj(C)))
-        if lr is None or isinstance(lr, float):
-            lr_dict = {}
-        else:
-            lr_dict, lr = lr, None
-        self.register("log_dt", log_dt, lr_dict.get("dt", lr))
-        self.register("B", _c2r(B), lr_dict.get("B", lr))
-        self.register("P", _c2r(P), lr_dict.get("A", lr))
-        self.register("inv_w_real", self._w_init(w.real), lr_dict.get("A", lr))
-        self.register("w_imag", w.imag, lr_dict.get("A", lr))
-
-        self.l_max = L
-        self.register_buffer("L", torch.tensor(0))  # Internal length
-
-    def _w_init(self, w_real):
-        w_real = torch.clamp(w_real, max=-self.real_tolerance)
-        if self.real_type == "none":
-            return -w_real
-        elif self.real_type == "exp":
-            return torch.log(
-                -w_real
-            )  # Some of the HiPPO methods have real part 0
-        elif self.real_type == "relu":
-            return -w_real
-        elif self.real_type == "sigmoid":
-            return torch.logit(-w_real)
-        elif self.real_type == "softplus":
-            return torch.log(torch.exp(-w_real) - 1)
-        else:
-            raise NotImplementedError
-
-    def _w(self):
-        # Get the internal w (diagonal) parameter
-        if self.real_type == "none":
-            w_real = -self.inv_w_real
-        elif self.real_type == "exp":
-            w_real = -torch.exp(self.inv_w_real)
-        elif self.real_type == "relu":
-            w_real = -F.relu(self.inv_w_real)
-        elif self.real_type == "sigmoid":
-            w_real = -F.sigmoid(self.inv_w_real)
-        elif self.real_type == "softplus":
-            w_real = -F.softplus(self.inv_w_real)
-        else:
-            raise NotImplementedError
-        w = w_real + 1j * self.w_imag
-        return w
-
-    def forward(self, state=None, rate=1.0, L=None):
-        """
-        state: (B, H, N) initial state
-        rate: sampling rate factor
-        L: target length
-
-        returns:
-        (C, H, L) convolution kernel (generally C=1)
-        (B, H, L) output from initial state
-        """
-
-        # Initialize C~ if necessary (done in forward pass so it's on the correct device)
-        if self.L.item() == 0 and self.l_max is not None and self.l_max > 0:
-            self._setup_C(self.l_max)
-
-        # Handle sampling rate logic
-        # The idea is that this kernel's length (in continuous units) is self.L, while we are asked to provide a kernel of length L at (relative) frequency rate
-        if L is None:
-            L = round(self.L.item() / rate)
-
-        # Increase the internal length if needed
-        continuous_L = round(rate * L)
-        while continuous_L > self.L.item():
-            self._setup_C(continuous_L)
-        discrete_L = round(self.L.item() / rate)
-
-        dt = torch.exp(self.log_dt) * rate
-        B = _r2c(self.B)
-        C = _r2c(self.C)
-        P = _r2c(self.P)
-        Q = P.conj()
-        w = self._w()  # (n_ssm, N)
-
-        # Address bandlimiting
-        if self.bandlimit is not None:
-            freqs = w.imag.abs() / (2 * math.pi)  # (H, N)
-            freqs = dt[:, None] / rate * freqs  # (H, N)
-            mask = torch.where(freqs < self.bandlimit * 0.5, 1, 0)
-            C = C * mask
-
-        # Get FFT nodes of right length
-        omega, z = self._omega(
-            discrete_L, dtype=w.dtype, device=w.device, cache=(rate == 1.0)
-        )
-
-        # Broadcast parameters to same hidden features H
-        B = repeat(B, "1 t n -> 1 (v t) n", v=self.repeat)
-        P = repeat(P, "r t n -> r (v t) n", v=self.repeat)
-        Q = repeat(Q, "r t n -> r (v t) n", v=self.repeat)
-        w = repeat(w, "t n -> (v t) n", v=self.repeat)
-
-        # Augment B
-        if state is not None:
-            # Have to "unbilinear" the state to put it into the same "type" as B
-            # Compute 1/dt * (I + dt/2 A) @ state
-
-            # Can do this without expanding (maybe minor speedup using conj symmetry in theory), but it's easier to read this way
-            s = _conj(state) if state.size(-1) == self.N else state  # (B H N)
-            sA = s * _conj(w) - contract(  # (B H N)
-                "bhm, rhm, rhn -> bhn", s, _conj(Q), _conj(P)
-            )
-            s = s / dt.unsqueeze(-1) + sA / 2
-            s = s[..., : self.N]
-
-            B = torch.cat([s, B], dim=-3)  # (B+1, H, N)
-
-        # Incorporate dt into A
-        w = w * dt.unsqueeze(-1)  # (H N)
-
-        # Stack B and p, C and q for convenient batching
-        B = torch.cat([B, P], dim=-3)  # (B+1+R, H, N)
-        C = torch.cat([C, Q], dim=-3)  # (C+R, H, N)
-
-        # Incorporate B and C batch dimensions
-        v = B.unsqueeze(-3) * C.unsqueeze(-4)  # (B+1+R, C+R, H, N)
-
-        # Calculate resolvent at omega
-        if has_cauchy_extension and z.dtype == torch.cfloat and not self.keops:
-            r = cauchy_mult(v, z, w, symmetric=True)
-        elif has_pykeops:
-            r = cauchy_conj(v, z, w)
-        else:
-            r = cauchy_naive(v, z, w)
-        r = r * dt[None, None, :, None]  # (B+1+R, C+R, H, L)
-
-        # Low-rank Woodbury correction
-        if self.rank == 1:
-            k_f = r[:-1, :-1, :, :] - r[:-1, -1:, :, :] * r[-1:, :-1, :, :] / (
-                1 + r[-1:, -1:, :, :]
-            )
-        elif self.rank == 2:
-            r00 = r[: -self.rank, : -self.rank, :, :]
-            r01 = r[: -self.rank, -self.rank :, :, :]
-            r10 = r[-self.rank :, : -self.rank, :, :]
-            r11 = r[-self.rank :, -self.rank :, :, :]
-            det = (1 + r11[:1, :1, :, :]) * (1 + r11[1:, 1:, :, :]) - r11[
-                :1, 1:, :, :
-            ] * r11[1:, :1, :, :]
-            s = (
-                r01[:, :1, :, :] * (1 + r11[1:, 1:, :, :]) * r10[:1, :, :, :]
-                + r01[:, 1:, :, :] * (1 + r11[:1, :1, :, :]) * r10[1:, :, :, :]
-                - r01[:, :1, :, :] * (r11[:1, 1:, :, :]) * r10[1:, :, :, :]
-                - r01[:, 1:, :, :] * (r11[1:, :1, :, :]) * r10[:1, :, :, :]
-            )
-            s = s / det
-            k_f = r00 - s
-        else:
-            r00 = r[: -self.rank, : -self.rank, :, :]
-            r01 = r[: -self.rank, -self.rank :, :, :]
-            r10 = r[-self.rank :, : -self.rank, :, :]
-            r11 = r[-self.rank :, -self.rank :, :, :]
-            r11 = rearrange(r11, "a b h n -> h n a b")
-            r11 = torch.linalg.inv(torch.eye(self.rank, device=r.device) + r11)
-            r11 = rearrange(r11, "h n a b -> a b h n")
-            k_f = r00 - torch.einsum(
-                "i j h n, j k h n, k l h n -> i l h n", r01, r11, r10
-            )
-
-        # Final correction for the bilinear transform
-        k_f = k_f * 2 / (1 + omega)
-
-        # Move from frequency to coefficients
-        k = torch.fft.irfft(k_f, n=discrete_L)  # (B+1, C, H, L)
-
-        # # Truncate to target length
-        k = k[..., :L]
-
-        if state is not None:
-            k_state = k[:-1, :, :, :]  # (B, C, H, L)
-        else:
-            k_state = None
-        k_B = k[-1, :, :, :]  # (C H L)
-
-        return k_B, k_state
-
-    @torch.no_grad()
-    def _setup_linear(self):
-        """Create parameters that allow fast linear stepping of state"""
-        w = self._w()
-        B = _r2c(self.B)  # (H N)
-        P = _r2c(self.P)
-        Q = P.conj()
-
-        # Repeat w shape properly
-        B = repeat(B, "1 t n -> 1 (v t) n", v=self.repeat)
-        P = repeat(P, "r t n -> r (v t) n", v=self.repeat)
-        Q = repeat(Q, "r t n -> r (v t) n", v=self.repeat)
-        w = repeat(w, "t n -> (v t) n", v=self.repeat)
-
-        # Prepare Linear stepping
-        dt = torch.exp(self.log_dt)
-        D = (2.0 / dt.unsqueeze(-1) - w).reciprocal()  # (H, N)
-        R = (
-            torch.eye(self.rank, dtype=w.dtype, device=w.device)
-            + 2 * contract("r h n, h n, s h n -> h r s", Q, D, P).real
-        )  # (H R R)
-        Q_D = rearrange(Q * D, "r h n -> h r n")
-        try:
-            R = torch.linalg.solve(R, Q_D)  # (H R N)
-        except Exception:
-            R = torch.tensor(
-                np.linalg.solve(
-                    R.to(Q_D).contiguous().detach().cpu(),
-                    Q_D.contiguous().detach().cpu(),
-                )
-            ).to(Q_D)
-        R = rearrange(R, "h r n -> r h n")
-
-        self.step_params = {
-            "D": D,  # (H N)
-            "R": R,  # (R H N)
-            "P": P,  # (R H N)
-            "Q": Q,  # (R H N)
-            "B": B,  # (1 H N)
-            "E": 2.0 / dt.unsqueeze(-1) + w,  # (H N)
-        }
-
-    def _step_state_linear(self, u=None, state=None):
-        """
-        Version of the step function that has time O(N) instead of O(N^2) per step, which takes advantage of the DPLR form and bilinear discretization.
-
-        Unfortunately, as currently implemented it's about 2x slower because it calls several sequential operations. Perhaps a fused CUDA kernel implementation would be much faster
-
-        u: (H) input
-        state: (H, N/2) state with conjugate pairs
-          Optionally, the state can have last dimension N
-        Returns: same shape as state
-        """
-        C = _r2c(self.C)  # View used for dtype/device
-
-        if u is None:  # Special case used to find dA
-            u = torch.zeros(self.H, dtype=C.dtype, device=C.device)
-        if state is None:  # Special case used to find dB
-            state = torch.zeros(self.H, self.N, dtype=C.dtype, device=C.device)
-
-        step_params = self.step_params.copy()
-        if (
-            state.size(-1) == self.N
-        ):  # Only store half of the conjugate pairs; should be true by default
-            # There should be a slightly faster way using conjugate symmetry
-            def contract_fn(p, x, y):
-                return contract(
-                    "r h n, r h m, ... h m -> ... h n",
-                    _conj(p),
-                    _conj(x),
-                    _conj(y),
-                )[
-                    ..., : self.N
-                ]  # inner outer product
-
-        else:
-            assert state.size(-1) == 2 * self.N
-            step_params = {k: _conj(v) for k, v in step_params.items()}
-
-            # TODO worth setting up a contract_expression in default_state if we want to use this at inference time for stepping
-            def contract_fn(p, x, y):
-                return contract(
-                    "r h n, r h m, ... h m -> ... h n", p, x, y
-                )  # inner outer product
-
-        D = step_params["D"]  # (H N)
-        E = step_params["E"]  # (H N)
-        R = step_params["R"]  # (R H N)
-        P = step_params["P"]  # (R H N)
-        Q = step_params["Q"]  # (R H N)
-        B = step_params["B"]  # (1 H N)
-
-        new_state = E * state - contract_fn(P, Q, state)  # (B H N)
-        new_state = new_state + 2.0 * B * u.unsqueeze(-1)  # (B H N)
-        new_state = D * (new_state - contract_fn(P, R, new_state))
-
-        return new_state
-
-    def _setup_state(self):
-        """Construct dA and dB for discretized state equation"""
-
-        # Construct dA and dB by using the stepping
-        self._setup_linear()
-        C = _r2c(
-            self.C
-        )  # Just returns a view that we use for finding dtype/device
-
-        state = torch.eye(
-            2 * self.N, dtype=C.dtype, device=C.device
-        ).unsqueeze(
-            -2
-        )  # (N 1 N)
-        dA = self._step_state_linear(state=state)
-        dA = rearrange(dA, "n h m -> h m n")
-
-        u = C.new_ones(self.H)
-        dB = self._step_state_linear(u=u)
-        dB = _conj(dB)
-        dB = rearrange(dB, "1 h n -> h n")  # (H N)
-        return dA, dB
-
-    def _step_state(self, u, state):
-        """Must be called after self.default_state() is used to construct an initial state!"""
-        next_state = self.state_contraction(
-            self.dA, state
-        ) + self.input_contraction(self.dB, u)
-        return next_state
-
-    def _setup_step(self, mode="dense"):
-        """Set up dA, dB, dC discretized parameters for stepping"""
-        self.dA, self.dB = self._setup_state()
-
-        # Calculate original C
-        C = _conj(_r2c(self.C))  # (H C N)
-        if self.L.item() == 0:
-            dC = C
-        else:
-            # self.C represents C_tilde
-            dA_L = power(self.L.item(), self.dA)
-            I = torch.eye(self.dA.size(-1)).to(dA_L)
-
-            dC = torch.linalg.solve(
-                I - dA_L.transpose(-1, -2),
-                C.unsqueeze(-1),
-            ).squeeze(-1)
-        self.dC = dC
-
-        # Do special preprocessing for different step modes
-
-        self._step_mode = mode
-        if mode == "linear":
-            # Linear case: special step function for the state, we need to handle output
-            # use conjugate symmetry by default, which affects the output projection
-            self.dC = 2 * self.dC[:, :, : self.N]
-        elif mode == "diagonal":
-            # Eigendecomposition of the A matrix
-            L, V = torch.linalg.eig(self.dA)
-            V_inv = torch.linalg.inv(V)
-            # Check that the eigendedecomposition is correct
-            if self.verbose:
-                print(
-                    "Diagonalization error:",
-                    torch.dist(V @ torch.diag_embed(L) @ V_inv, self.dA),
-                )
-
-            # Change the parameterization to diagonalize
-            self.dA = L
-            self.dB = contract("h n m, h m -> h n", V_inv, self.dB)
-            self.dC = contract("h n m, c h n -> c h m", V, self.dC)
-
-        elif mode == "dense":
-            pass
-        else:
-            raise NotImplementedError(
-                "NPLR Kernel step mode must be {'dense' | 'linear' | 'diagonal'}"
-            )
-
-    def default_state(self, *batch_shape):
-        C = _r2c(self.C)
-        N = C.size(-1)
-        H = C.size(-2)
-
-        # Cache the tensor contractions we will later do, for efficiency
-        # These are put in this function because they depend on the batch size
-        step_mode = getattr(
-            self, "_step_mode", "dense"
-        )  # Used in default_state, which is called without _setup_step() in forward_state()
-        if step_mode != "linear":
-            N *= 2
-
-            if step_mode == "diagonal":
-                self.state_contraction = contract_expression(
-                    "h n, ... h n -> ... h n",
-                    (H, N),
-                    batch_shape + (H, N),
-                )
-            else:
-                # Dense (quadratic) case: expand all terms
-                self.state_contraction = contract_expression(
-                    "h m n, ... h n -> ... h m",
-                    (H, N, N),
-                    batch_shape + (H, N),
-                )
-
-            self.input_contraction = contract_expression(
-                "h n, ... h -> ... h n",
-                (H, N),  # self.dB.shape
-                batch_shape + (H,),
-            )
-
-        self.output_contraction = contract_expression(
-            "c h n, ... h n -> ... c h",
-            (C.shape[0], H, N),  # self.dC.shape
-            batch_shape + (H, N),
-        )
-
-        state = torch.zeros(*batch_shape, H, N, dtype=C.dtype, device=C.device)
-        return state
-
-    def step(self, u, state):
-        """Must have called self._setup_step() and created state with self.default_state() before calling this"""
-
-        if self._step_mode == "linear":
-            new_state = self._step_state_linear(u, state)
-        else:
-            new_state = self._step_state(u, state)
-        y = self.output_contraction(self.dC, new_state)
-        return y.real, new_state
-
-
-class SSKernelDiag(OptimModule):
-    """Version using (complex) diagonal state matrix (S4D)"""
-
-    def __init__(
-        self,
-        A,
-        B,
-        C,
-        log_dt,
-        L=None,
-        disc="bilinear",
-        real_type="exp",
-        lr=None,
-        bandlimit=None,
-    ):
-        super().__init__()
-        self.L = L
-        self.disc = disc
-        self.bandlimit = bandlimit
-        self.real_type = real_type
-
-        # Rank of low-rank correction
-        assert A.size(-1) == C.size(-1)
-        self.H = log_dt.size(-1)
-        self.N = A.size(-1)
-        assert A.size(-2) == B.size(-2)  # Number of independent SSMs trained
-        assert self.H % A.size(-2) == 0
-        self.n_ssm = A.size(-2)
-        self.repeat = self.H // A.size(0)
-
-        self.channels = C.shape[0]
-        self.C = nn.Parameter(_c2r(_resolve_conj(C)))
-
-        # Register parameters
-        if lr is None or isinstance(lr, float):
-            lr_dict = {}
-        else:
-            lr_dict, lr = lr, None
-
-        self.register("log_dt", log_dt, lr_dict.get("dt", lr))
-        self.register("B", _c2r(B), lr_dict.get("B", lr))
-        self.register("inv_A_real", self._A_init(A.real), lr_dict.get("A", lr))
-        self.register("A_imag", A.imag, lr_dict.get("A", lr))
-
-    def _A_init(self, A_real):
-        A_real = torch.clamp(A_real, max=-1e-4)
-        if self.real_type == "none":
-            return -A_real
-        elif self.real_type == "exp":
-            return torch.log(
-                -A_real
-            )  # Some of the HiPPO methods have real part 0
-        elif self.real_type == "relu":
-            return -A_real
-        elif self.real_type == "sigmoid":
-            return torch.logit(-A_real)
-        elif self.real_type == "softplus":
-            return torch.log(torch.exp(-A_real) - 1)
-        else:
-            raise NotImplementedError
-
-    def _A(self):
-        # Get the internal A (diagonal) parameter
-        if self.real_type == "none":
-            A_real = -self.inv_A_real
-        elif self.real_type == "exp":
-            A_real = -torch.exp(self.inv_A_real)
-        elif self.real_type == "relu":
-            # JAX version seems to NaN if you alloA 0's, although this code Aas fine Aithout it
-            A_real = -F.relu(self.inv_A_real) - 1e-4
-        elif self.real_type == "sigmoid":
-            A_real = -F.sigmoid(self.inv_A_real)
-        elif self.real_type == "softplus":
-            A_real = -F.softplus(self.inv_A_real)
-        else:
-            raise NotImplementedError
-        A = A_real + 1j * self.A_imag
-        return A
-
-    def forward(self, L, state=None, rate=1.0, u=None):
-        """
-        state: (B, H, N) initial state
-        rate: sampling rate factor
-        L: target length
-
-        returns:
-        (C, H, L) convolution kernel (generally C=1)
-        (B, H, L) output from initial state
-        """
-
-        dt = torch.exp(self.log_dt) * rate  # (H)
-        C = _r2c(self.C)  # (C H N)
-        A = self._A()  # (H N)
-
-        B = _r2c(self.B)
-        B = repeat(B, "t n -> 1 (v t) n", v=self.repeat)
-
-        if self.bandlimit is not None:
-            freqs = dt[:, None] / rate * A.imag.abs() / (2 * math.pi)  # (H, N)
-            mask = torch.where(freqs < self.bandlimit * 0.5, 1, 0)
-            C = C * mask
-
-        # Incorporate dt into A
-        A = repeat(A, "t n -> (v t) n", v=self.repeat)
-        dtA = A * dt.unsqueeze(-1)  # (H N)
-
-        # Augment B with state
-        if state is not None:
-            s = state / dt.unsqueeze(-1)
-            if self.disc == "bilinear":
-                s = s * (1.0 + dtA / 2)
-            elif self.disc == "zoh":
-                s = s * dtA * dtA.exp() / (dtA.exp() - 1.0)
-            B = torch.cat([s, B], dim=-3)  # (1+B H N)
-
-        C = (B[:, None, :, :] * C).view(-1, self.H, self.N)
-        if self.disc == "zoh":
-            # Power up
-            C = C * (torch.exp(dtA) - 1.0) / A
-            K = log_vandermonde(C, dtA, L)  # (H L)
-        elif self.disc == "bilinear":
-            C = (
-                C * (1.0 - dtA / 2).reciprocal() * dt.unsqueeze(-1)
-            )  # or * dtA / A
-            dA = (1.0 + dtA / 2) / (1.0 - dtA / 2)
-            K = log_vandermonde(C, dA.log(), L)
-        elif self.disc == "dss":
-            # Implementation from DSS meant for case when real eigenvalues can be positive
-            P = dtA.unsqueeze(-1) * torch.arange(L, device=C.device)  # [H N L]
-            A_gt_0 = A.real > 0  # [N]
-            if A_gt_0.any():
-                with torch.no_grad():
-                    P_max = dtA * (A_gt_0 * (L - 1))  # [H N]
-                P = P - P_max.unsqueeze(-1)  # [H N L]
-            S = P.exp()  # [H N L]
-
-            dtA_neg = dtA * (1 - 2 * A_gt_0)  # [H N]
-            num = dtA_neg.exp() - 1  # [H N]
-            den = (dtA_neg * L).exp() - 1  # [H N]
-
-            # Inline reciprocal function for DSS logic
-            x = den * A
-            x_conj = _resolve_conj(x)
-            r = x_conj / (x * x_conj + 1e-7)
-
-            C = C * num * r  # [C H N]
-            K = contract("chn,hnl->chl", C, S).float()
-        else:
-            assert False, f"{self.disc} not supported"
-
-        K = K.view(-1, self.channels, self.H, L)  # (1+B C H L)
-        if state is not None:
-            K_state = K[:-1, :, :, :]  # (B C H L)
-        else:
-            K_state = None
-        K = K[-1, :, :, :]  # (C H L)
-        return K, K_state
-
-    def _setup_step(self):
-        # These methods are organized like this to be compatible with the NPLR kernel interface
-        dt = torch.exp(self.log_dt)  # (H)
-        B = _r2c(self.B)  # (H N)
-        C = _r2c(self.C)  # (C H N)
-        self.dC = C
-        A = self._A()  # (H N)
-
-        A = repeat(A, "t n -> (v t) n", v=self.repeat)
-        B = repeat(B, "t n -> (v t) n", v=self.repeat)
-
-        # Incorporate dt into A
-        dtA = A * dt.unsqueeze(-1)  # (H N)
-        if self.disc == "zoh":
-            self.dA = torch.exp(dtA)  # (H N)
-            self.dB = B * (torch.exp(dtA) - 1.0) / A  # (C H N)
-        elif self.disc == "bilinear":
-            self.dA = (1.0 + dtA / 2) / (1.0 - dtA / 2)
-            self.dB = (
-                B * (1.0 - dtA / 2).reciprocal() * dt.unsqueeze(-1)
-            )  # or * dtA / A
-
-    def default_state(self, *batch_shape):
-        C = _r2c(self.C)
-        state = torch.zeros(
-            *batch_shape, self.H, self.N, dtype=C.dtype, device=C.device
-        )
-        return state
-
-    def step(self, u, state):
-        next_state = contract(
-            "h n, b h n -> b h n", self.dA, state
-        ) + contract("h n, b h -> b h n", self.dB, u)
-        y = contract("c h n, b h n -> b c h", self.dC, next_state)
-        return 2 * y.real, next_state
-
-    def forward_state(self, u, state):
-        self._setup_step()
-        AL = self.dA ** u.size(-1)
-        u = u.flip(-1).to(self.dA).contiguous()  # (B H L)
-        v = log_vandermonde_transpose(u, self.dB, self.dA.log(), u.size(-1))
-        next_state = AL * state + v
-        return next_state
-
-
-class SSKernel(nn.Module):
-    """Wrapper around SSKernel parameterizations.
-
-    The SSKernel is expected to support the interface
-    forward()
-    default_state()
-    _setup_step()
-    step()
-    """
-
-    def __init__(
-        self,
-        H,
-        N=64,
-        L=None,
-        measure="legs",
-        rank=1,
-        channels=1,
-        dt_min=0.001,
-        dt_max=0.1,
-        deterministic=False,
-        lr=None,
-        mode="nplr",
-        n_ssm=None,
-        verbose=False,
-        measure_args={},
-        **kernel_args,
-    ):
-        """State Space Kernel which computes the convolution kernel $\\bar{K}$
-
-        H: Number of independent SSM copies; controls the size of the model. Also called d_model in the config.
-        N: State size (dimensionality of parameters A, B, C). Also called d_state in the config. Generally shouldn't need to be adjusted and doens't affect speed much.
-        L: Maximum length of convolution kernel, if known. Should work in the majority of cases even if not known.
-        measure: Options for initialization of (A, B). For NPLR mode, recommendations are "legs", "fout", "hippo" (combination of both). For Diag mode, recommendations are "diag-inv", "diag-lin", "diag-legs", and "diag" (combination of diag-inv and diag-lin)
-        rank: Rank of low-rank correction for NPLR mode. Needs to be increased for measure "legt"
-        channels: C channels turns the SSM from a 1-dim to C-dim map; can think of it having C separate "heads" per SSM. This was partly a feature to make it easier to implement bidirectionality; it is recommended to set channels=1 and adjust H to control parameters instead
-        dt_min, dt_max: min and max values for the step size dt (\Delta)
-        mode: Which kernel algorithm to use. 'nplr' is the full S4 model; 'diag' is the simpler S4D; 'slow' is a dense version for testing
-        n_ssm: Number of independent trainable (A, B) SSMs, e.g. n_ssm=1 means all A/B parameters are tied across the H different instantiations of C. n_ssm=None means all H SSMs are completely independent. Generally, changing this option can save parameters but doesn't affect performance or speed much. This parameter must divide H
-        lr: Passing in a number (e.g. 0.001) sets attributes of SSM parameers (A, B, dt). A custom optimizer hook is needed to configure the optimizer to set the learning rates appropriately for these parameters.
-        """
-        super().__init__()
-        self.N = N
-        self.H = H
-        dtype, cdtype = torch.float, torch.cfloat
-        self.channels = channels
-        self.n_ssm = n_ssm if n_ssm is not None else H
-        self.mode = mode
-        self.verbose = verbose
-        self.kernel_args = kernel_args
-
-        # Generate dt
-        if deterministic:
-            log_dt = torch.exp(
-                torch.linspace(math.log(dt_min), math.log(dt_max), H)
-            )
-        else:
-            log_dt = torch.rand(self.H, dtype=dtype) * (
-                math.log(dt_max) - math.log(dt_min)
-            ) + math.log(dt_min)
-
-        # Compute the preprocessed representation
-        w, P, B, V = combination(
-            measure, self.N, rank, self.n_ssm, **measure_args
-        )
-
-        # Broadcast C to have H channels
-        if deterministic:
-            C = torch.zeros(channels, self.n_ssm, self.N, dtype=cdtype)
-            C[:, :, :1] = 1.0
-            C = contract(
-                "hmn, chn -> chm", V.conj().transpose(-1, -2), C
-            )  # V^* C
-            C = (
-                repeat(C, "c t n -> c (v t) n", v=self.n_ssm // C.size(-2))
-                .clone()
-                .contiguous()
-            )
-        else:
-            C = torch.randn(channels, self.H, self.N // 2, dtype=cdtype)
-
-        # Broadcast other parameters to have n_ssm copies
-        assert (
-            self.n_ssm % B.size(-2) == 0
-            and self.n_ssm % P.size(-2) == 0
-            and self.n_ssm % w.size(-2) == 0
-        )
-        # Broadcast tensors to n_ssm copies
-        # These will be the parameters, so make sure tensors are materialized and contiguous
-        B = (
-            repeat(B, "t n -> (v t) n", v=self.n_ssm // B.size(-2))
-            .clone()
-            .contiguous()
-        )
-        P = (
-            repeat(P, "r t n -> r (v t) n", v=self.n_ssm // P.size(-2))
-            .clone()
-            .contiguous()
-        )
-        w = (
-            repeat(w, "t n -> (v t) n", v=self.n_ssm // w.size(-2))
-            .clone()
-            .contiguous()
-        )
-
-        if mode == "nplr":
-            self.kernel = SSKernelNPLR(
-                w,
-                P,
-                B,
-                C,
-                log_dt,
-                L=L,
-                lr=lr,
-                verbose=verbose,
-                **kernel_args,
-            )
-        elif mode == "diag":
-            if not measure.startswith("diag"):
-                log.warning(
-                    "Diagonal kernel (S4D) activated but initialization is not intended for S4D. Set `measure` to 'diag-lin', 'diag-inv', or 'diag-legs' for the main variants, or 'diag' for a combination of S4D-Lin and S4D-Inv."
-                )
-            C = C * repeat(B, "t n -> (v t) n", v=H // self.n_ssm)
-            self.kernel = SSKernelDiag(
-                w,
-                B,
-                C,
-                log_dt,
-                L=L,
-                lr=lr,
-                **kernel_args,
-            )
-        else:
-            raise NotImplementedError(f"{mode=} is not valid")
-
-    def forward(self, state=None, L=None, rate=1.0):
-        return self.kernel(state=state, L=L, rate=rate)
-
-    @torch.no_grad()
-    def forward_state(self, u, state):
-        """Forward the state through a sequence, i.e. computes the state after passing chunk through SSM
-
-        state: (B, H, N)
-        u: (B, H, L)
-
-        Returns: (B, H, N)
-        """
-
-        if hasattr(self.kernel, "forward_state"):
-            return self.kernel.forward_state(u, state)
-
-        dA, dB = self.kernel._setup_state()  # Construct dA, dB matrices
-        # dA, dB = self.kernel.dA, self.kernel.dB # (H N N) (H N)
-
-        conj = state.size(-1) != dA.size(-1)
-        if conj:
-            state = _conj(state)
-
-        v = contract(
-            "h n, b h l -> b h n l", dB, u.flip(-1)
-        )  # dB.unsqueeze(-1) * u.flip(-1).unsqueeze(-2)
-        AL, v = power(u.size(-1), dA, v)
-        next_state = contract("h m n, b h n -> b h m", AL, state)
-        next_state = next_state + v
-
-        if conj:
-            next_state = next_state[..., : next_state.size(-1) // 2]
-        return next_state
-
-    def _setup_step(self, **kwargs):
-        # This method is intended to be private so that setting up an S4 module with
-        # ```
-        # if hasattr(module, 'setup_step'): module.setup_step()
-        # ```
-        # will not trigger this method multiple times
-        self.kernel._setup_step(**kwargs)
-
-    def step(self, u, state, **kwargs):
-        y, state = self.kernel.step(u, state, **kwargs)
-        return y, state
-
-    def default_state(self, *args, **kwargs):
-        return self.kernel.default_state(*args, **kwargs)
-
-
-class S4(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        d_state=64,
-        l_max=None,
-        channels=1,
-        mode="nplr",
-        measure="legs",
-        bidirectional=False,
-        # Arguments for position-wise feedforward components
-        activation="gelu",
-        postact="glu",
-        hyper_act=None,
-        dropout=0.0,
-        tie_dropout=False,
-        bottleneck=None,
-        gate=None,
-        transposed=True,
-        verbose=False,
-        # SSM Kernel arguments
-        **kernel_args,
-    ):
-        """
-        d_state: the dimension of the state, also denoted by N
-        l_max: the maximum kernel length, also denoted by L. Set l_max=None to always use a global kernel
-        channels: can be interpreted as a number of "heads"; the SSM is a map from a 1-dim to C-dim sequence. It's not recommended to change this unless desperate for things to tune; instead, increase d_model for larger models
-        bidirectional: if True, convolution kernel will be two-sided
-
-        Position-wise feedforward components:
-        --------------------
-        activation: activation in between SS and FF
-        postact: activation after FF
-        hyper_act: use a "hypernetwork" multiplication (experimental)
-        dropout: standard dropout argument. tie_dropout=True ties the dropout mask across the sequence length, emulating nn.Dropout1d
-
-        Other arguments:
-        --------------------
-        transposed: choose backbone axis ordering of (B, L, H) (if False) or (B, H, L) (if True) [B=batch size, L=sequence length, H=hidden dimension]
-        gate: add gated activation (GSS)
-        bottleneck: reduce SSM dimension (GSS)
-
-        See the class SSKernel for the kernel constructor which accepts kernel_args. Relevant options that are worth considering and tuning include "mode" + "measure", "dt_min", "dt_max", "lr"
-
-        Other options are all experimental and should not need to be configured
-        """
-
-        super().__init__()
-        if verbose:
-            log.info(
-                f"Constructing S4 (H, N, L) = ({d_model}, {d_state}, {l_max})"
-            )
-
-        self.d_model = d_model
-        self.H = d_model
-        self.N = d_state
-        self.L = l_max
-        self.bidirectional = bidirectional
-        self.channels = channels
-        self.transposed = transposed
-
-        self.gate = gate
-        self.bottleneck = bottleneck
-
-        if bottleneck is not None:
-            self.H = self.H // bottleneck
-            self.input_linear = LinearActivation(
-                self.d_model,
-                self.H,
-                transposed=self.transposed,
-                activation=activation,
-                activate=True,
-            )
-
-        if gate is not None:
-            self.input_gate = LinearActivation(
-                self.d_model,
-                self.d_model * gate,
-                transposed=self.transposed,
-                activation=activation,
-                activate=True,
-            )
-            self.output_gate = LinearActivation(
-                self.d_model * gate,
-                self.d_model,
-                transposed=self.transposed,
-                activation=None,
-                activate=False,
-            )
-
-        # optional multiplicative modulation GLU-style
-        # https://arxiv.org/abs/2002.05202
-        self.hyper = hyper_act is not None
-        if self.hyper:
-            channels *= 2
-            self.hyper_activation = Activation(hyper_act)
-
-        self.D = nn.Parameter(torch.randn(channels, self.H))
-
-        if self.bidirectional:
-            channels *= 2
-
-        # SSM Kernel
-        self.kernel = SSKernel(
-            self.H,
-            N=self.N,
-            L=self.L,
-            channels=channels,
-            verbose=verbose,
-            mode=mode,
-            measure=measure,
-            **kernel_args,
-        )
-
-        # Pointwise
-        self.activation = Activation(activation)
-        dropout_fn = DropoutNd if tie_dropout else nn.Dropout
-        self.dropout = dropout_fn(dropout) if dropout > 0.0 else nn.Identity()
-        # position-wise output transform to mix features
-        self.output_linear = LinearActivation(
-            self.H * self.channels,
-            self.d_model * (1 if self.gate is None else self.gate),
-            transposed=self.transposed,
-            activation=postact,
-            activate=True,
-        )
-
-    def forward(self, u, state=None, rate=1.0, lengths=None, **kwargs):
-        """
-        u: (B H L) if self.transposed else (B L H)
-        state: (H N) never needed unless you know what you're doing
-
-        Returns: same shape as u
-        """
-        if not self.transposed:
-            u = u.transpose(-1, -2)
-        L = u.size(-1)
-
-        # Mask out padding tokens
-        if isinstance(lengths, int):
-            if lengths != L:
-                lengths = torch.tensor(
-                    lengths, dtype=torch.long, device=u.device
-                )
-            else:
-                lengths = None
-        if lengths is not None:
-            assert (
-                isinstance(lengths, torch.Tensor)
-                and lengths.ndim == 1
-                and lengths.size(0) in [1, u.size(0)]
-            )
-            mask = torch.where(
-                torch.arange(L, device=lengths.device)
-                < lengths[:, None, None],
-                1.0,
-                0.0,
-            )
-            u = u * mask
-
-        if self.gate is not None:
-            v = self.input_gate(u)
-        if self.bottleneck is not None:
-            u = self.input_linear(u)
-
-        # Compute SS Kernel
-        L_kernel = L if self.L is None else min(L, round(self.L / rate))
-        k, k_state = self.kernel(
-            L=L_kernel, rate=rate, state=state
-        )  # (C H L) (B C H L)
-
-        # Convolution
-        if self.bidirectional:
-            k0, k1 = rearrange(k, "(s c) h l -> s c h l", s=2)
-            k = F.pad(k0, (0, L)) + F.pad(k1.flip(-1), (L, 0))
-        k_f = torch.fft.rfft(k, n=L_kernel + L)  # (C H L)
-        u_f = torch.fft.rfft(u, n=L_kernel + L)  # (B H L)
-        y_f = contract("bhl,chl->bchl", u_f, k_f)
-        y = torch.fft.irfft(y_f, n=L_kernel + L)[..., :L]  # (B C H L)
-
-        # Compute D term in state space equation - essentially a skip connection
-        y = y + contract("bhl,ch->bchl", u, self.D)
-
-        # Compute state update
-        if state is not None:
-            assert (
-                not self.bidirectional
-            ), "Bidirectional not supported with state forwarding"
-            y = y + k_state  #
-            next_state = self.kernel.forward_state(u, state)
-        else:
-            next_state = None
-
-        # Optional hyper-network multiplication
-        if self.hyper:
-            y, yh = rearrange(y, "b (s c) h l -> s b c h l", s=2)
-            y = self.hyper_activation(yh) * y
-
-        # Reshape to flatten channels
-        y = rearrange(y, "... c h l -> ... (c h) l")
-
-        y = self.dropout(self.activation(y))
-
-        if not self.transposed:
-            y = y.transpose(-1, -2)
-
-        y = self.output_linear(y)
-
-        if self.gate is not None:
-            y = self.output_gate(y * v)
-
-        return y, next_state
-
-    def setup_step(self, **kwargs):
-        self.kernel._setup_step(**kwargs)
-
-    def step(self, u, state):
-        """Step one time step as a recurrent model. Intended to be used during validation.
-
-        u: (B H)
-        state: (B H N)
-        Returns: output (B H), state (B H N)
-        """
-        assert not self.training
-
-        y, next_state = self.kernel.step(u, state)  # (B C H)
-        y = y + u.unsqueeze(-2) * self.D
-        y = rearrange(y, "b c h -> b (c h)")
-        y = self.activation(y)
-        if self.transposed:
-            y = self.output_linear(y.unsqueeze(-1)).squeeze(-1)
-        else:
-            y = self.output_linear(y)
-        return y, next_state
-
-    def default_state(self, *batch_shape, device=None):
-        # kernel is not a SequenceModule so it doesn't need to adhere to same interface
-        # the kernel will know the device of its own parameters
-        return self.kernel.default_state(*batch_shape)
-
-    @property
-    def d_output(self):
-        return self.d_model
diff --git a/probts/model/nn/layers/s4_backbones.py b/probts/model/nn/layers/s4_backbones.py
deleted file mode 100644
index 20bed5c..0000000
--- a/probts/model/nn/layers/s4_backbones.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-# SPDX-License-Identifier: Apache-2.0
-import math
-
-import torch
-from torch import nn
-
-from .s4 import S4
-
-
-class SinusoidalPositionEmbeddings(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, time):
-        device = time.device
-        half_dim = self.dim // 2
-        embeddings = math.log(10000) / (half_dim - 1)
-        embeddings = torch.exp(
-            torch.arange(half_dim, device=device) * -embeddings
-        )
-        embeddings = time[:, None] * embeddings[None, :]
-        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
-        return embeddings
-
-
-class S4Layer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        dropout=0.0,
-        mode="nplr",
-        l_max=None,
-        measure="legs"
-    ):
-        super().__init__()
-        self.layer = S4(
-            d_model=d_model,
-            d_state=128,
-            bidirectional=True,
-            dropout=dropout,
-            transposed=True,
-            postact=None,
-            mode=mode,
-            l_max=l_max,
-            measure=measure,
-        )
-        self.norm = nn.LayerNorm(d_model)
-        self.dropout = (
-            nn.Dropout1d(dropout) if dropout > 0.0 else nn.Identity()
-        )
-
-    def forward(self, x):
-        """
-        Input x is shape (B, d_input, L)
-        """
-        z = x
-        # Prenorm
-        z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
-        # Apply layer: we ignore the state input and output for training
-        z, _ = self.layer(z)
-        # Dropout on the output of the layer
-        z = self.dropout(z)
-        # Residual connection
-        x = z + x
-        return x, None
-
-    def default_state(self, *args, **kwargs):
-        return self.layer.default_state(*args, **kwargs)
-
-    def step(self, x, state, **kwargs):
-        z = x
-        # Prenorm
-        z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
-        # Apply layer
-        z, state = self.layer.step(z, state, **kwargs)
-        # Residual connection
-        x = z + x
-        return x, state
-
-
-class S4Block(nn.Module):
-    def __init__(self, d_model, dropout=0.0, expand=2, num_features=0,mode="nplr",l_max=None,measure="legs"):
-        super().__init__()
-        self.s4block = S4Layer(d_model, dropout=dropout,mode=mode,l_max=l_max,measure=measure)
-
-        self.time_linear = nn.Linear(d_model, d_model)
-        self.tanh = nn.Tanh()
-        self.sigm = nn.Sigmoid()
-        self.out_linear1 = nn.Conv1d(
-            in_channels=d_model, out_channels=d_model, kernel_size=1
-        )
-        self.out_linear2 = nn.Conv1d(
-            in_channels=d_model, out_channels=d_model, kernel_size=1
-        )
-        self.feature_encoder = nn.Conv1d(num_features, d_model, kernel_size=1)
-
-    def forward(self, x, t, features=None):
-        t = self.time_linear(t)[:, None, :].repeat(1, x.shape[2], 1)
-        t = t.transpose(-1, -2)
-        out, _ = self.s4block(x + t)
-        if features is not None:
-            out = out + self.feature_encoder(features)
-        out = self.tanh(out) * self.sigm(out)
-        out1 = self.out_linear1(out)
-        out2 = self.out_linear2(out)
-        return out1 + x, out2
-
-
-def Conv1dKaiming(in_channels, out_channels, kernel_size):
-    layer = nn.Conv1d(in_channels, out_channels, kernel_size)
-    nn.init.kaiming_normal_(layer.weight)
-    return layer
-
-
-class BackboneModel(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        hidden_dim,
-        output_dim,
-        step_emb,
-        num_residual_blocks,
-        num_features,
-        residual_block="s4",
-        mode="nplr",
-        measure="legs",
-        l_max=None,
-        dropout=0.0,
-        init_skip=True,
-    ):
-        super().__init__()
-        if residual_block == "s4":
-            residual_block = S4Block
-        else:
-            raise ValueError(f"Unknown residual block {residual_block}")
-        self.input_init = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            nn.ReLU(),
-        )
-        self.time_init = nn.Sequential(
-            nn.Linear(step_emb, hidden_dim),
-            nn.SiLU(),
-            nn.Linear(hidden_dim, hidden_dim),
-            nn.SiLU(),
-        )
-        self.out_linear = nn.Sequential(
-            nn.Linear(hidden_dim, hidden_dim),
-            nn.ReLU(),
-            nn.Linear(hidden_dim, output_dim),
-        )
-        residual_blocks = []
-        for i in range(num_residual_blocks):
-            residual_blocks.append(
-                residual_block(
-                    hidden_dim, 
-                    num_features=num_features, 
-                    dropout=dropout, 
-                    mode=mode,l_max=l_max,
-                    measure=measure,
-                )
-            )
-        self.residual_blocks = nn.ModuleList(residual_blocks)
-        self.step_embedding = SinusoidalPositionEmbeddings(step_emb)
-        self.init_skip = init_skip
-
-    def forward(self, input, t, features=None):
-        x = self.input_init(input)  # B, L ,C
-        t = self.time_init(self.step_embedding(t))
-        x = x.transpose(-1, -2)
-        if features is not None:
-            features = features.transpose(-1, -2)
-        skips = []
-        for layer in self.residual_blocks:
-            x, skip = layer(x, t, features)
-            skips.append(skip)
-
-        skip = torch.stack(skips).sum(0)
-        skip = skip.transpose(-1, -2)
-        out = self.out_linear(skip)
-        if self.init_skip:
-            out = out + input
-        return out
diff --git a/probts/model/reweight_forecast_module.py b/probts/model/reweight_forecast_module.py
new file mode 100644
index 0000000..0b3f9b5
--- /dev/null
+++ b/probts/model/reweight_forecast_module.py
@@ -0,0 +1,180 @@
+import numpy as np
+import torch
+from torch import optim
+from typing import Dict
+import lightning.pytorch as pl
+import sys
+from probts.data import ProbTSBatchData
+from probts.model.forecaster import Forecaster
+from probts.utils import Evaluator, Scaler
+from probts.utils.metrics import *
+from probts.utils.save_utils import update_metrics, calculate_average, save_point_error, load_checkpoint, get_hor_str
+
+
+def get_weights(sampling_weight_scheme, max_hor):
+    '''
+    return: w [max_hor]
+    '''
+    if sampling_weight_scheme == 'random':
+        i_array = np.linspace(1 + 1e-5, max_hor - 1e-3, max_hor)
+        w = (1 / max_hor) * (np.log(max_hor) - np.log(i_array))
+    elif sampling_weight_scheme == 'const':
+        w = np.array([1 / max_hor] * max_hor)
+    elif sampling_weight_scheme == 'none':
+        return None
+    else:
+        raise ValueError(f"Invalid sampling scheme {sampling_weight_scheme}.")
+    
+    return torch.tensor(w)
+    
+
+class HorizonReweightForecaster(pl.LightningModule):
+    def __init__(
+        self,
+        forecaster: Forecaster,
+        scaler: Scaler = None,
+        pred_len_list: list = None,
+        num_samples: int = 100,
+        learning_rate: float = 1e-3,
+        quantiles_num: int = 10,
+        load_from_ckpt: str = None,
+        save_point_error: bool = False,
+        sampling_weight_scheme: str = 'none',
+        **kwargs
+    ):
+        super().__init__()
+        self.num_samples = num_samples
+        self.learning_rate = learning_rate
+        self.load_from_ckpt = load_from_ckpt
+        self.forecaster = forecaster
+        
+        self.max_hor = max(pred_len_list)
+        self.min_hor = min(pred_len_list)
+        self.save_point_error = save_point_error
+            
+        self.scaler = scaler
+        self.evaluator = Evaluator(quantiles_num=quantiles_num)
+        
+        # init the parapemetr for sampling
+        self.sampling_weight_scheme = sampling_weight_scheme
+
+        print(f'sampling_weight_scheme: {sampling_weight_scheme}')
+
+        self.save_hyperparameters()
+
+    @classmethod
+    def load_from_checkpoint(self, checkpoint_path, scaler=None, learning_rate=None, no_training=False, **kwargs):
+        model = load_checkpoint(self, checkpoint_path, scaler=scaler, learning_rate=learning_rate, no_training=no_training, **kwargs)
+        return model
+
+    def training_forward(self, batch_data):
+        batch_data.past_target_cdf = self.scaler.transform(batch_data.past_target_cdf)
+        batch_data.future_target_cdf = self.scaler.transform(batch_data.future_target_cdf)
+        
+        loss = self.forecaster.loss(batch_data)
+
+        loss_weights = get_weights(self.sampling_weight_scheme, self.max_hor)
+        if loss_weights is not None:
+            loss = (loss_weights.detach().to(loss.device).unsqueeze(0).unsqueeze(-1) * loss).sum(dim=1)
+
+        loss = loss.mean()
+        return loss
+        
+
+    def training_step(self, batch, batch_idx):
+        batch_data = ProbTSBatchData(batch, self.device)
+        loss = self.training_forward(batch_data)
+        self.log("train_loss", loss, on_step=True, prog_bar=True, logger=True)
+        return loss
+
+
+    def evaluate(self, batch, stage='',dataloader_idx=None):
+        batch_data = ProbTSBatchData(batch, self.device)
+        pred_len = batch_data.future_target_cdf.shape[1]
+        orin_past_data = batch_data.past_target_cdf[:]
+        orin_future_data = batch_data.future_target_cdf[:]
+
+        norm_past_data = self.scaler.transform(batch_data.past_target_cdf)
+        norm_future_data = self.scaler.transform(batch_data.future_target_cdf)
+        
+        batch_data.past_target_cdf = self.scaler.transform(batch_data.past_target_cdf)
+        forecasts = self.forecaster.forecast(batch_data, self.num_samples)[:,:, :pred_len]
+        
+        # Calculate denorm metrics
+        denorm_forecasts = self.scaler.inverse_transform(forecasts)
+        
+        # Calculate norm metrics
+        norm_metrics = self.evaluator(norm_future_data, forecasts, past_data=norm_past_data, freq=self.forecaster.freq)
+        self.metrics_dict = update_metrics(norm_metrics, stage, 'norm', target_dict=self.metrics_dict)
+        
+        
+        l = orin_future_data.shape[1]
+        
+        if stage != 'test' and self.sampling_weight_scheme not in ['fix', 'none']:
+            loss_weights = get_weights('random', l)
+        else:
+            loss_weights = None
+            
+        metrics = self.evaluator(orin_future_data, denorm_forecasts, past_data=orin_past_data, freq=self.forecaster.freq, loss_weights=loss_weights)
+        self.metrics_dict = update_metrics(metrics, stage, target_dict=self.metrics_dict)
+        
+        if stage == 'test':
+            hor_str = get_hor_str(self.forecaster.prediction_length, dataloader_idx)
+            if hor_str not in self.hor_metrics:
+                self.hor_metrics[hor_str] = {}
+
+            if self.save_point_error:
+                self.point_error = save_point_error(orin_future_data.cpu().detach().numpy(), 
+                                                    denorm_forecasts[:,0,:].cpu().detach().numpy(), 
+                                                    self.point_error, hor_str)
+                
+            self.hor_metrics[hor_str] = update_metrics(metrics, stage, target_dict=self.hor_metrics[hor_str])
+
+        return metrics
+
+
+    def validation_step(self, batch, batch_idx, dataloader_idx=None):
+        metrics = self.evaluate(batch, stage='val',dataloader_idx=dataloader_idx)
+        
+        return metrics
+
+
+    def on_validation_epoch_start(self):
+        self.metrics_dict = {}
+        self.hor_metrics = {}
+        self.point_error = {}
+
+    def on_validation_epoch_end(self):
+        avg_metrics = calculate_average(self.metrics_dict)
+        self.log_dict(avg_metrics, prog_bar=True) 
+
+
+    def test_step(self, batch, batch_idx, dataloader_idx=None):
+        metrics = self.evaluate(batch, stage='test',dataloader_idx=dataloader_idx)
+        return metrics
+
+    def on_test_epoch_start(self):
+        self.metrics_dict = {}
+        self.hor_metrics = {}
+        self.avg_metrics = {}
+        self.avg_hor_metrics = {}
+        self.point_error = {}
+
+    def on_test_epoch_end(self):
+        if len(self.hor_metrics) > 0:
+            for hor_str, metric in self.hor_metrics.items():
+                self.avg_hor_metrics[hor_str] = calculate_average(metric)
+                self.avg_metrics.update(calculate_average(metric, hor=hor_str))
+        else:
+            self.avg_metrics = calculate_average(self.metrics_dict)
+            
+
+    def predict_step(self, batch, batch_idx):
+        batch_data = ProbTSBatchData(batch, self.device)
+        forecasts = self.forecaster.forecast(batch_data, self.num_samples)
+        return forecasts
+
+    def configure_optimizers(self):
+        optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
+        return optimizer
+    
diff --git a/probts/utils/evaluator.py b/probts/utils/evaluator.py
index 63dd373..5198b6f 100644
--- a/probts/utils/evaluator.py
+++ b/probts/utils/evaluator.py
@@ -18,7 +18,7 @@ def weighted_loss_name(self, q):
     def coverage_name(self, q):
         return f"Coverage[{q}]"
 
-    def get_sequence_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1):
+    def get_sequence_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1,loss_weights=None):
         mean_forecasts = forecasts.mean(axis=samples_dim)
         median_forecasts = np.quantile(forecasts, 0.5, axis=samples_dim)
         metrics = {
@@ -37,6 +37,15 @@ def get_sequence_metrics(self, targets, forecasts, seasonal_error=None, samples_
         metrics["NRMSE"] = metrics["RMSE"] / metrics["abs_target_mean"]
         metrics["ND"] = metrics["abs_error"] / metrics["abs_target_sum"]
         
+        # calculate weighted loss
+        if loss_weights is not None:
+            nd = np.abs(targets - mean_forecasts) / np.sum(np.abs(targets), axis=(1, 2))
+            loss_weights = loss_weights.detach().unsqueeze(0).unsqueeze(-1).numpy()
+            weighted_ND = loss_weights * nd
+            metrics['weighted_ND'] = np.sum(weighted_ND)
+        else:
+            metrics['weighted_ND'] = metrics["ND"]
+
         for q in self.quantiles:
             q_forecasts = np.quantile(forecasts, q, axis=samples_dim)
             metrics[self.loss_name(q)] = quantile_loss(targets, q_forecasts, q)
@@ -58,7 +67,7 @@ def get_sequence_metrics(self, targets, forecasts, seasonal_error=None, samples_
         )
         return metrics
 
-    def get_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1):
+    def get_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1, loss_weights=None):
         metrics = {}
         seq_metrics = {}
         
@@ -68,7 +77,8 @@ def get_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1):
                 np.expand_dims(targets[i], axis=0),
                 np.expand_dims(forecasts[i], axis=0),
                 np.expand_dims(seasonal_error[i], axis=0) if seasonal_error is not None else None,
-                samples_dim
+                samples_dim,
+                loss_weights
             )
             for metric_name, metric_value in single_seq_metrics.items():
                 if metric_name not in seq_metrics:
@@ -81,9 +91,9 @@ def get_metrics(self, targets, forecasts, seasonal_error=None, samples_dim=1):
 
     @property
     def selected_metrics(self):
-        return ["CRPS", "ND", "NRMSE", "MSE", "MASE"]
+        return [ "ND",'weighted_ND', 'CRPS', "NRMSE", "MSE", "MASE"]
 
-    def __call__(self, targets, forecasts, past_data, freq):
+    def __call__(self, targets, forecasts, past_data, freq, loss_weights=None):
         """
 
         Parameters
@@ -106,7 +116,7 @@ def __call__(self, targets, forecasts, past_data, freq):
         
         seasonal_error = calculate_seasonal_error(past_data, freq)
 
-        metrics = self.get_metrics(targets, forecasts, seasonal_error=seasonal_error, samples_dim=1)
+        metrics = self.get_metrics(targets, forecasts, seasonal_error=seasonal_error, samples_dim=1, loss_weights=loss_weights)
         metrics_sum = self.get_metrics(targets.sum(axis=-1), forecasts.sum(axis=-1), samples_dim=1)
         
         # select output metrics
diff --git a/probts/utils/metrics.py b/probts/utils/metrics.py
index 3fdbc7a..f192497 100644
--- a/probts/utils/metrics.py
+++ b/probts/utils/metrics.py
@@ -64,7 +64,7 @@ def mase(
     diff = np.mean(np.abs(target - forecast), axis=1)
     mase = diff / seasonal_error
     # if seasonal_error is 0, set mase to 0
-    mase = mase.filled(0)  
+    mase = np.nan_to_num(mase, nan=0.0)
     return np.mean(mase)
 
 def calculate_seasonal_error(
diff --git a/probts/utils/position_emb.py b/probts/utils/position_emb.py
new file mode 100644
index 0000000..af73633
--- /dev/null
+++ b/probts/utils/position_emb.py
@@ -0,0 +1,59 @@
+import torch
+from torch import nn
+import numpy as np
+from einops import rearrange, repeat
+
+class Time_Encoder(nn.Module):
+    def __init__(self, embed_time):
+        super(Time_Encoder, self).__init__()
+        self.periodic = nn.Linear(1, embed_time - 1)
+        self.linear = nn.Linear(1, 1)
+
+    def forward(self, tt):
+        if tt.dim() == 3:  # [B,L,K]
+            tt = rearrange(tt, 'b l k -> b l k 1')
+        else: # [B,L]
+            tt = rearrange(tt, 'b l -> b l 1 1')
+        
+        out2 = torch.sin(self.periodic(tt))
+        out1 = self.linear(tt)
+        out = torch.cat([out1, out2], -1) # [B,L,1,D]
+        return out
+    
+def sin_cos_encoding(B, K, L, embed_dim):
+    assert embed_dim % 2 == 0
+    
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+    pos= [i for i in range(L)]
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    
+    emb = repeat(emb, 'l d -> b k l d', b=B, k=K)
+    return torch.tensor(emb, dtype=torch.float64)
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
\ No newline at end of file
diff --git a/probts/utils/save_utils.py b/probts/utils/save_utils.py
new file mode 100644
index 0000000..876a02e
--- /dev/null
+++ b/probts/utils/save_utils.py
@@ -0,0 +1,155 @@
+from typing import Dict
+import numpy as np
+import torch
+from probts.model.forecaster import Forecaster
+import importlib
+import json
+import pandas as pd
+import pickle
+import os
+
+def update_metrics(new_metrics: Dict, stage: str, key: str = '', target_dict = {}):
+    prefix = stage if key == '' else f'{stage}_{key}'
+    for metric_name, metric_value in new_metrics.items():
+        metric_key = f'{prefix}_{metric_name}'
+        if metric_key not in target_dict:
+            target_dict[metric_key] = []
+            
+        if isinstance(metric_value, list):
+            target_dict[metric_key] = target_dict[metric_key] + metric_value
+        else:
+            target_dict[metric_key].append(metric_value)
+        
+    return target_dict
+
+def calculate_average(metrics_dict: Dict, hor=''):
+    metrics = {}
+    if hor != '':
+        hor = hor + '/'
+    # print("metrics_dict: ", metrics_dict)
+    for key, value in metrics_dict.items():
+        metrics[hor+key] = np.mean(value)
+    return metrics
+
+
+def calculate_weighted_average(metrics_dict: Dict, batch_size: list):
+    metrics = {}
+    for key, value in metrics_dict.items():
+        metrics[key] = np.sum(value * np.array(batch_size)) / np.sum(batch_size)
+    return metrics
+
+def save_point_error(target, predict, input_dict, hor_str):
+    if hor_str not in input_dict:
+        input_dict[hor_str] = {'MAE': [], 'target': [], 'forecast': []}
+    
+    abs_error = np.abs(target - predict)
+
+    input_dict[hor_str]['MAE'].append(abs_error)
+    input_dict[hor_str]['target'].append(target)
+    input_dict[hor_str]['forecast'].append(predict)
+    return input_dict
+
+
+def load_checkpoint(Model, checkpoint_path, scaler=None, learning_rate=None, no_training=False, **kwargs):
+    # Load the checkpoint
+    checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
+    # Extract the arguments for the forecaster
+    forecaster_args = checkpoint['hyper_parameters']['forecaster']
+
+    if isinstance(forecaster_args, Forecaster):
+        forecaster = forecaster_args
+    else:
+        module_path, class_name = forecaster_args['class_path'].rsplit('.', 1)
+        forecaster_class = getattr(importlib.import_module(module_path), class_name)
+        
+        # Add any missing required arguments
+        forecaster_args = forecaster_args['init_args']
+        forecaster_args.update(kwargs)
+        
+        # Create the forecaster
+        forecaster = forecaster_class(**forecaster_args)
+    
+    forecaster.no_training = no_training
+    
+    if learning_rate is None:
+        learning_rate = checkpoint['hyper_parameters'].get('learning_rate', 1e-3)
+    
+    # Create the model instance
+    model = Model(
+        forecaster=forecaster,
+        scaler=scaler,
+        num_samples=checkpoint['hyper_parameters'].get('num_samples', 100),
+        learning_rate=learning_rate,
+        quantiles_num=checkpoint['hyper_parameters'].get('quantiles_num', 10),
+        load_from_ckpt=checkpoint['hyper_parameters'].get('load_from_ckpt', None),
+        **kwargs  # Pass additional arguments here
+    )
+    model.load_state_dict(checkpoint['state_dict'])
+    return model
+
+def get_hor_str(prediction_length, dataloader_idx):
+    if dataloader_idx is not None:
+        hor_str = str(prediction_length[dataloader_idx])
+    elif type(prediction_length) == list:
+        hor_str = str(prediction_length[0])
+    else:
+        hor_str = str(prediction_length)
+    return hor_str
+
+
+def save_exp_summary(pl_module, inference=False):
+    exp_summary = {}
+    
+    model_summary = pl_module.model_summary_callback._summary(pl_module.trainer, pl_module.model)
+    exp_summary['total_parameters'] = model_summary.total_parameters
+    exp_summary['trainable_parameters'] = model_summary.trainable_parameters
+    exp_summary['model_size'] = model_summary.model_size
+    
+    memory_summary = pl_module.memory_callback.memory_summary
+    exp_summary['memory_summary'] = memory_summary
+    
+    time_summary = pl_module.time_callback.time_summary
+    exp_summary['time_summary'] = time_summary
+    for batch_key, batch_time in time_summary.items():
+        if len(batch_time) > 0:
+            exp_summary[f'mean_{batch_key}'] = sum(batch_time) / len(batch_time)
+    
+    exp_summary['sampling_weight_scheme'] = pl_module.model.sampling_weight_scheme
+    
+    if inference:
+        summary_save_path = f"{pl_module.save_dict}/inference_summary.json"
+    else:
+        summary_save_path = f"{pl_module.save_dict}/summary.json"
+
+    with open(summary_save_path, 'w') as f:
+        json.dump(exp_summary, f, indent=4)
+    print(f"Summary saved to {summary_save_path}")
+    
+    
+def save_csv(save_dict, model, context_length):
+    if len(model.avg_hor_metrics) > 0:
+        horizon_list = []
+        for horizon in model.avg_hor_metrics:
+            horizon_dict = model.avg_hor_metrics[str(horizon)]
+            horizon_dict['horizon'] = horizon
+            horizon_list.append(horizon_dict)
+            
+        df = pd.DataFrame(horizon_list)
+        
+    else:
+        df = pd.DataFrame([model.avg_metrics])
+    
+    if not model.forecaster.no_training:
+        test_result_file = 'multi_horizon_test'
+    else:
+        test_result_file = f'testctx_{context_length}_multi_horizon_test'
+        
+    df.to_csv(f'{save_dict}/{test_result_file}.csv', index='idx')
+    print('horizons result saved to ', f'{save_dict}/{test_result_file}.csv')
+    
+    if model.save_point_error:
+        output_file_path = os.path.join(save_dict, 'point_nd.pkl')
+        with open(output_file_path, 'wb') as pkl_file:
+            pickle.dump(model.point_error, pkl_file)
+            
+        print('point error saved to ', output_file_path)
\ No newline at end of file
diff --git a/probts/utils/utils.py b/probts/utils/utils.py
index 927388a..3b68ffc 100644
--- a/probts/utils/utils.py
+++ b/probts/utils/utils.py
@@ -6,11 +6,12 @@
 # We thank the authors for their contributions.
 # ---------------------------------------------------------------------------------
 
-
+import re
+import os
 import torch
 import numpy as np
 from typing import Optional, Dict
-
+import torch.nn as nn
 
 class Scaler:
     def __init__(self):
@@ -201,7 +202,8 @@ def extract(a, t, x_shape):
 def weighted_average(
     x: torch.Tensor,
     weights: Optional[torch.Tensor] = None,
-    dim: int = None
+    dim: int = None,
+    reduce: str = 'mean',
 ):
     """
     Computes the weighted average of a given tensor across a given dim, masking
@@ -218,6 +220,8 @@ def weighted_average(
     """
     if weights is not None:
         weighted_tensor = torch.where(weights != 0, x * weights, torch.zeros_like(x))
+        if reduce != 'mean':
+            return weighted_tensor
         sum_weights = torch.clamp(
             weights.sum(dim=dim) if dim else weights.sum(), min=1.0
         )
@@ -225,4 +229,203 @@ def weighted_average(
             weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()
         ) / sum_weights
     else:
-        return x.mean(dim=dim)
\ No newline at end of file
+        return x.mean(dim=dim) if dim else x
+    
+    
+class InstanceNorm(nn.Module):
+    def __init__(self, eps=1e-5):
+        """
+        :param eps: a value added for numerical stability
+        """
+        super(InstanceNorm, self).__init__()
+        self.eps = eps
+
+    def forward(self, x, mode:str):
+        if mode == 'norm':
+            self._get_statistics(x)
+            x = self._normalize(x)
+        elif mode == 'denorm':
+            x = self._denormalize(x)
+        else: raise NotImplementedError
+        return x
+
+    def _get_statistics(self, x):
+        dim2reduce = tuple(range(1, x.ndim-1))
+        self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
+        self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()
+
+    def _normalize(self, x):
+        x = x - self.mean
+        x = x / self.stdev
+        return x
+
+    def _denormalize(self, x):
+        x = x * self.stdev
+        x = x + self.mean
+        return x
+
+   
+class RamdonRevIN(nn.Module):
+    def __init__(self, num_features: int, eps=1e-5, dist="gaussian"):
+        """
+        :param num_features: the number of features or channels
+        :param eps: a value added for numerical stability
+        :param affine: if True, RevIN has learnable affine parameters
+        """
+        super(RamdonRevIN, self).__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.dist = dist
+        self._init_params()
+
+    def forward(self, x, mode:str, mask=None):
+        dim = len(x.shape)
+        if dim == 4 and x.shape[1] == 1:
+            x = x.squeeze(1)
+            if mask is not None:
+                mask = mask.squeeze(1)
+            
+        if mode == 'norm':
+            self._get_statistics(x,mask)
+            x = self._normalize(x) * mask
+        elif mode == 'denorm':
+            x = self._denormalize(x) * mask
+        else: raise NotImplementedError
+
+        if dim == 4 and len(x.shape)==3:
+            x = x.unsqueeze(1)
+        return x
+
+    def _init_params(self):
+        # initialize RevIN params
+        requires_grad = False
+        if self.dist == "gaussian":
+            affine_weight = torch.randn(self.num_features) + torch.ones(self.num_features)
+            affine_bias = torch.randn(self.num_features)
+        elif self.dist == "learn":
+            affine_weight = torch.ones(self.num_features)
+            affine_bias = torch.zeros(self.num_features)
+            requires_grad = True
+        elif self.dist == "uniform" or self.dist == "none":
+            affine_weight = torch.rand(self.num_features)
+            affine_bias = torch.rand(self.num_features)
+        else:
+            print("Invalid distribution setting for RamdonRevIN, select from: [gaussian, learn, uniform, none]")
+            
+        if requires_grad:
+            self.affine_weight = nn.Parameter(affine_weight, requires_grad=True)
+            self.affine_bias = nn.Parameter(affine_bias,requires_grad=True)
+        else:
+            self.register_buffer('affine_weight', torch.tensor(affine_weight))
+            self.register_buffer('affine_bias', torch.tensor(affine_bias))
+                
+        
+
+    def _get_statistics(self, x, mask):
+        dim2reduce = tuple(range(1, x.ndim-1))
+        if mask is None:
+            self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
+            self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()
+        else:
+            num_observed = mask.sum(dim=dim2reduce, keepdim=True)
+            self.mean = ((x * mask).sum(dim=dim2reduce, keepdim=True) / num_observed).detach()
+            squared_diffs = (x - self.mean) ** 2 * mask
+            variance = squared_diffs.sum(dim=dim2reduce, keepdim=True) / num_observed  
+            self.stdev = torch.sqrt(variance + self.eps).detach()
+
+    def _normalize(self, x):
+        x = x - self.mean
+        x = x / self.stdev
+
+        x = x * self.affine_weight
+        x = x + self.affine_bias
+        return x
+
+    def _denormalize(self, x):
+        x = x - self.affine_bias
+        x = x / (self.affine_weight + self.eps*self.eps)
+        x = x * self.stdev
+        x = x + self.mean
+        return x
+
+
+def convert_to_list(s):
+    '''
+    Convert prediction length strings into list
+    e.g., '96-192-336-720' will be convert into [96,192,336,720]
+    Input: str, list, int
+    Returns: list
+    '''
+    if (type(s).__name__=='int'):
+        return [s]
+    elif (type(s).__name__=='list'):
+        return s
+    elif (type(s).__name__=='str'):
+        elements = re.split(r'\D+', s)
+        return list(map(int, elements))
+    else:
+        return None
+    
+def convert_to_float_list(s):
+    '''
+    Convert prediction length strings into list
+    e.g., '96-192-336-720' will be convert into [96,192,336,720]
+    Input: str, list, int
+    Returns: list
+    '''
+    if (type(s).__name__=='int'):
+        return [s]
+    elif (type(s).__name__=='list'):
+        return s
+    elif (type(s).__name__=='str'):
+        elements = re.split('-', s)
+        return list(map(float, elements))
+    else:
+        return None
+    
+def find_min_prediction_length(arr, x):
+    if x is None:
+        return x
+    for value in arr:
+        if value >= x:
+            return value
+    return None
+
+
+def find_best_epoch(ckpt_folder):
+    """
+    Find the highest epoch in the Test Tube file structure.
+    :param ckpt_folder: dir where the checpoints are being saved.
+    :return: Integer of the highest epoch reached by the checkpoints.
+    """
+    pattern = r"val_CRPS=([0-9]*\.[0-9]+)"
+    ckpt_files = os.listdir(ckpt_folder)  # list of strings
+    epochs = []
+    for filename in ckpt_files:
+        match = re.search(pattern, filename)
+        if match:  
+            epochs.append(match.group(1))
+    # epochs = [float(filename[18:-5]) for filename in ckpt_files]  # 'epoch={int}.ckpt' filename format
+    best_crps = min(epochs)
+    
+    best_epoch = epochs.index(best_crps)
+    return best_epoch, ckpt_files[best_epoch]
+
+
+def get_wandb_config_dict(wandb, config_args):
+    config_dict = {}
+    args = config_args.model.forecaster
+
+    if "init_args" in args:
+        for key, value in args["init_args"].items():
+            if wandb['target_config'] is not None and key in wandb['target_config']:
+                # continue
+                config_dict[key] = value
+
+    config_dict["dataset"] = config_args.data.data_manager["init_args"]["dataset"]
+    config_dict["pred_len"] = config_args.data.data_manager["init_args"]["prediction_length"]
+    config_dict["context_length"] = config_args.data.data_manager["init_args"]["context_length"]
+    config_dict["train_pred_len"] = config_args.data.data_manager["init_args"]["train_pred_len_list"]
+    config_dict["train_ctx_len_list"] = config_args.data.data_manager["init_args"]["train_ctx_len_list"]
+    config_dict["val_pred_len_list"] = config_args.data.data_manager["init_args"]["val_pred_len_list"]
+    return config_dict
\ No newline at end of file
diff --git a/run.py b/run.py
index 59477a6..bf1db5b 100644
--- a/run.py
+++ b/run.py
@@ -2,12 +2,15 @@
 import json
 import torch
 import logging
-from probts.data import ProbTSDataModule
+from probts.data import ProbTSDataModule, ElasTSTDataModule 
 from probts.model.forecast_module import ProbTSForecastModule
+from probts.model.reweight_forecast_module import HorizonReweightForecaster
 from probts.callbacks import MemoryCallback, TimeCallback
+from probts.utils import find_best_epoch
 from lightning.pytorch.cli import LightningCLI
 from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
 from lightning.pytorch.callbacks import ModelCheckpoint
+from probts.utils.save_utils import save_exp_summary, save_csv
 
 import warnings
 warnings.filterwarnings('ignore')
@@ -19,21 +22,20 @@
 
 
 class ProbTSCli(LightningCLI):
-    
     def add_arguments_to_parser(self, parser):
         data_to_model_link_args = [
-            "scaler"
+            "scaler",
+            "pred_len_list", 
         ]
         data_to_forecaster_link_args = [
             "target_dim",
             "history_length",
             "context_length",
             "prediction_length",
+            "pred_len_list", 
             "lags_list",
             "freq",
             "time_feat_dim",
-            "global_mean",
-            "dataset"
         ]
         for arg in data_to_model_link_args:
             parser.link_arguments(f"data.data_manager.{arg}", f"model.{arg}", apply_on="instantiate")
@@ -44,19 +46,33 @@ def init_exp(self):
         config_args = self.parser.parse_args()
         self.tag = "_".join([
             self.datamodule.data_manager.dataset,
-            str(self.datamodule.data_manager.context_length),
-            str(self.datamodule.data_manager.prediction_length),
             self.model.forecaster.name,
-            str(config_args.seed_everything)
+            str(config_args.seed_everything),
+            'CTX','-'.join([str(i) for i in self.datamodule.data_manager.ctx_len_list]),
+            'PRED','-'.join([str(i) for i in self.datamodule.data_manager.pred_len_list]),
+            'VALCTX','-'.join([str(i) for i in self.datamodule.data_manager.context_length]),
+            'VALPRED','-'.join([str(i) for i in self.datamodule.data_manager.val_pred_len_list]),
         ])
-        log.info(f"Root dir is {self.trainer.default_root_dir}, exp tag is {self.tag}")
         
+        log.info(f"Root dir is {self.trainer.default_root_dir}, exp tag is {self.tag}")
+
         if not os.path.exists(self.trainer.default_root_dir):
             os.makedirs(self.trainer.default_root_dir)
+            
+        self.save_dict = f'{self.trainer.default_root_dir}/{self.tag}'
+        if not os.path.exists(self.save_dict):
+            os.makedirs(self.save_dict)
 
         if self.model.load_from_ckpt is not None:
+            # if the checkpoint file is not assigned, find the best epoch in the current folder
+            if '.ckpt' not in self.model.load_from_ckpt:
+                _, best_ckpt = find_best_epoch(self.model.load_from_ckpt)
+                print("find best ckpt ", best_ckpt)
+                self.model.load_from_ckpt = os.path.join(self.model.load_from_ckpt, best_ckpt)
+            
+            # loading the checkpoints
             log.info(f"Loading pre-trained checkpoint from {self.model.load_from_ckpt}")
-            self.model = ProbTSForecastModule.load_from_checkpoint(
+            self.model = HorizonReweightForecaster.load_from_checkpoint(
                 self.model.load_from_ckpt,
                 learning_rate=config_args.model.learning_rate,
                 scaler=self.datamodule.data_manager.scaler,
@@ -64,29 +80,40 @@ def init_exp(self):
                 target_dim=self.datamodule.data_manager.target_dim,
                 freq=self.datamodule.data_manager.freq,
                 prediction_length=self.datamodule.data_manager.prediction_length,
+                pred_len_list=self.datamodule.data_manager.pred_len_list,
                 lags_list=self.datamodule.data_manager.lags_list,
                 time_feat_dim=self.datamodule.data_manager.time_feat_dim,
                 no_training=self.model.forecaster.no_training,
+                save_point_error=self.model.save_point_error,
+                sampling_weight_scheme=self.model.sampling_weight_scheme,
             )
-        
+
         # Set callbacks
-        self.checkpoint_callback = ModelCheckpoint(
-            dirpath=f'{self.trainer.default_root_dir}/ckpt/{self.tag}',
-            filename='{epoch}-{val_CRPS:.3f}',
-            every_n_epochs=1,
-            monitor='val_CRPS',
-            save_top_k=-1,
-            save_last=True,
-            enable_version_counter=False
-        )
+        callbacks = []
+        
+        if self.model.sampling_weight_scheme in ['none', 'fix']:
+            monitor = 'val_CRPS'
+        else:
+            monitor = 'val_weighted_ND'
+        
+        if not self.model.forecaster.no_training:
+            # Set callbacks
+            self.checkpoint_callback = ModelCheckpoint(
+                dirpath=f'{self.save_dict}/ckpt',
+                filename='{epoch}-{val_CRPS:.6f}',
+                every_n_epochs=1,
+                monitor=monitor,
+                save_top_k=-1,
+                save_last=True,
+                enable_version_counter=False
+            )
+            callbacks.append(self.checkpoint_callback)
+
         self.memory_callback = MemoryCallback()
         self.time_callback = TimeCallback()
         
-        callbacks = [
-            self.checkpoint_callback,
-            self.memory_callback,
-            self.time_callback
-        ]
+        callbacks.append(self.memory_callback)
+        callbacks.append(self.time_callback)
         self.set_callbacks(callbacks)
 
     def set_callbacks(self, callbacks):
@@ -103,14 +130,14 @@ def set_callbacks(self, callbacks):
 
     def set_fit_mode(self):
         self.trainer.logger = TensorBoardLogger(
-            save_dir=f'{self.trainer.default_root_dir}/logs',
+            save_dir=f'{self.save_dict}/logs',
             name=self.tag,
             version='fit'
         )
     
     def set_test_mode(self):
         self.trainer.logger = CSVLogger(
-            save_dir=f'{self.trainer.default_root_dir}/logs',
+            save_dir=f'{self.save_dict}/logs',
             name=self.tag,
             version='test'
         )
@@ -118,57 +145,42 @@ def set_test_mode(self):
         if not self.model.forecaster.no_training:
             self.ckpt = self.checkpoint_callback.best_model_path
             log.info(f"Loading best checkpoint from {self.ckpt}")
-            self.model = ProbTSForecastModule.load_from_checkpoint(
+            
+            self.model = HorizonReweightForecaster.load_from_checkpoint(
                 self.ckpt, 
                 scaler=self.datamodule.data_manager.scaler,
                 context_length=self.datamodule.data_manager.context_length,
                 target_dim=self.datamodule.data_manager.target_dim,
                 freq=self.datamodule.data_manager.freq,
                 prediction_length=self.datamodule.data_manager.prediction_length,
+                pred_len_list=self.datamodule.data_manager.pred_len_list,
                 lags_list=self.datamodule.data_manager.lags_list,
                 time_feat_dim=self.datamodule.data_manager.time_feat_dim,
+                sampling_weight_scheme=self.model.sampling_weight_scheme,
             )
-
-    def save_exp_summary(self):
-        exp_summary = {}
-        
-        model_summary = self.model_summary_callback._summary(self.trainer, self.model)
-        exp_summary['total_parameters'] = model_summary.total_parameters
-        exp_summary['trainable_parameters'] = model_summary.trainable_parameters
-        exp_summary['model_size'] = model_summary.model_size
-        
-        memory_summary = self.memory_callback.memory_summary
-        exp_summary['memory_summary'] = memory_summary
-        
-        time_summary = self.time_callback.time_summary
-        exp_summary['time_summary'] = time_summary
-        for batch_key, batch_time in time_summary.items():
-            if len(batch_time) > 0:
-                exp_summary[f'mean_{batch_key}'] = sum(batch_time) / len(batch_time)
-        
-        summary_save_path = f"{self.trainer.default_root_dir}/logs/{self.tag}/summary.json"
-        with open(summary_save_path, 'w') as f:
-            json.dump(exp_summary, f, indent=4)
-        log.info(f"Summary saved to {summary_save_path}")
-        
+    
     def run(self):
         self.init_exp()
         
         if not self.model.forecaster.no_training:
             self.set_fit_mode()
             self.trainer.fit(model=self.model, datamodule=self.datamodule)
+            inference=False
+        else:
+            inference=True
 
         self.set_test_mode()
         self.trainer.test(model=self.model, datamodule=self.datamodule)
         
-        if not self.model.forecaster.no_training:
-            self.save_exp_summary()
+        # saving multi testing horizon results
+        save_exp_summary(self, inference=inference)
+        save_csv(self.save_dict, self.model, self.datamodule.data_manager.context_length[0])
 
 
 if __name__ == '__main__':
     cli = ProbTSCli(
-        datamodule_class=ProbTSDataModule,
-        model_class=ProbTSForecastModule,
+        datamodule_class=ElasTSTDataModule,
+        model_class=HorizonReweightForecaster,
         save_config_kwargs={"overwrite": True},
         run=False
     )
diff --git a/run.sh b/run.sh
index c9c36fd..0377425 100644
--- a/run.sh
+++ b/run.sh
@@ -1,38 +1,25 @@
-export CUDA_VISIBLE_DEVICES=0
-MODEL=patchtst
-DATASET=etth1
-CTX_LEN=96
-PRED_LEN=96
-
-# DATA_DIR=/path/to/datasets
-# LOG_DIR=/path/to/log_dir
-
-DATA_DIR=./datasets
-LOG_DIR=../../log_dir
-
-# multivariate datasets:
-# ['exchange_rate_nips', 'solar_nips','electricity_nips', 'traffic_nips','wiki2000_nips']
+DATA_DIR=/path/to/datasets
+LOG_DIR=/path/to/log_dir
 
-# Univariate datasets:
-# ['m4_weekly', 'm4_hourly', 'm4_daily', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5', 'tourism_monthly', 'tourism_quarterly', 'tourism_yearly']
-
-# Long-term forecasting:
-# ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf']
-# NOTE: when using long-term forecasting datasets, please explicit assign context_length and prediction_length, e.g., :
-# --data.data_manager.init_args.context_length 96 \
-# --data.data_manager.init_args.prediction_length 192 \
+CTX_LEN=96
+TRAIN_CTX_LEN=96
+TRAIN_PRED_LEN=720
+VAL_PRED_LEN=720
+TEST_PRED_LEN=24-48-96-192-336-720-1024
 
-# run pipeline with train and test
-# replace ${MODEL} with tarfet model name, e.g, patchtst
-# replace ${DATASET} with dataset name
+# select from ['etth1', 'etth2', 'ettm1', 'ettm2', 'traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'weather_ltsf']
+DATASET='exchange_ltsf'
 
-# if not specify dataset_path, the default path is ./datasets
+MODEL=elastst
 
-python run.py --config config/ltsf/${DATASET}/${MODEL}.yaml --seed_everything 0  \
+python run.py --config config/ablation_study/${MODEL}.yaml --seed_everything 0  \
     --data.data_manager.init_args.path ${DATA_DIR} \
     --trainer.default_root_dir ${LOG_DIR} \
-    --data.data_manager.init_args.dataset ${DATASET} \
     --data.data_manager.init_args.split_val true \
-    --trainer.max_epochs 50 \
+    --data.data_manager.init_args.dataset ${DATASET} \
     --data.data_manager.init_args.context_length ${CTX_LEN} \
-    --data.data_manager.init_args.prediction_length ${PRED_LEN} 
+    --data.data_manager.init_args.prediction_length ${TEST_PRED_LEN} \
+    --data.data_manager.init_args.train_pred_len_list ${TRAIN_PRED_LEN} \
+    --data.data_manager.init_args.train_ctx_len_list ${TRAIN_CTX_LEN} \
+    --data.data_manager.init_args.val_pred_len_list ${VAL_PRED_LEN} \
+    --trainer.max_epochs 50
diff --git a/scripts/prepare_tsfm_checkpoints.sh b/scripts/prepare_tsfm_checkpoints.sh
deleted file mode 100644
index dafddf1..0000000
--- a/scripts/prepare_tsfm_checkpoints.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/sh
-
-echo "NOTE! By downloading these checkpoints, you agree to the licenses of the original models and checkpoints."
-echo ""
-echo "- [Timer](https://github.com/thuml/Large-Time-Series-Model) created by thuml. The original model and its checkpoints are licensed under the MIT License. The checkpoints are distributed under the MIT License. You may not use these files except in compliance with the License. You may obtain a copy of the License at: https://github.com/thuml/Large-Time-Series-Model/blob/main/LICENSE."
-echo "- [ForecastPFN](https://github.com/abacusai/ForecastPFN) created by abacusai. The original model and its checkpoints are licensed under the MIT License. The checkpoints are distributed under the Apache-2.0 License. You may not use these files except in compliance with the License. You may obtain a copy of the License at: https://github.com/abacusai/ForecastPFN/blob/main/LICENSE."
-echo "- [UniTS](https://github.com/mims-harvard/UniTS) created by mims-harvard. The original model and its checkpoints are licensed under the MIT License. The checkpoints are distributed under the MIT License. You may not use these files except in compliance with the License. You may obtain a copy of the License at: https://github.com/mims-harvard/UniTS/blob/main/LICENSE."
-echo "- [Lag-Llama](https://github.com/time-series-foundation-models/lag-llama) created by time-series-foundation-models. The original model and its checkpoints are licensed under the MIT License. The checkpoints are distributed under the Apache-2.0 License. You may not use these files except in compliance with the License. You may obtain a copy of the License at: https://github.com/time-series-foundation-models/lag-llama/blob/main/LICENSE."
-echo ""
-echo "NOTE! By downloading these checkpoints, you agree to the licenses of the original models and checkpoints."
-read -p "Do you want to continue? (yes/y to continue): " confirm
-
-# Convert input to lowercase for comparison
-confirm=$(echo "$confirm" | tr '[:upper:]' '[:lower:]')
-
-if [ "$confirm" = "yes" ] || [ "$confirm" = "y" ]; then
-    # Check if gdown is installed
-    if pip show gdown > /dev/null 2>&1; then
-        echo "gdown is already installed, skipping installation."
-    else
-        echo "gdown is not installed, installing..."
-        pip install gdown
-    fi
-    # Download the folder
-    gdown --folder 1FaCk9Lj9KZGEO09gehNqC4fbTj4wnN8j -O checkpoints
-else
-    echo "Download canceled."
-fi
\ No newline at end of file
diff --git a/scripts/reproduce_ltsf_results.sh b/scripts/reproduce_ltsf_results.sh
deleted file mode 100644
index bf67b69..0000000
--- a/scripts/reproduce_ltsf_results.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-export CUDA_VISIBLE_DEVICES=0
-
-DATA_DIR=./datasets
-LOG_DIR=./exps
-
-CTX_LEN=96
-
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf' 'electricity_ltsf' 'exchange_ltsf' 'traffic_ltsf'
-do
-    for MODEL in 'dlinear' 'patchtst' 'gru_nvp' 'timegrad' 'csdi'
-    do
-        for PRED_LEN in 96 192 336 720
-        do
-            python run.py --config config/ltsf/${DATASET}/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} 
-        done
-    done
-done
-
-CTX_LEN=36
-
-for DATASET in 'illness_ltsf'
-do
-    for MODEL in 'dlinear' 'patchtst' 'gru_nvp' 'timegrad' 'csdi'
-    do
-        for PRED_LEN in 24 36 48 60
-        do
-            python run.py --config config/ltsf/${DATASET}/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} 
-        done
-    done
-done
\ No newline at end of file
diff --git a/scripts/reproduce_stsf_results.sh b/scripts/reproduce_stsf_results.sh
deleted file mode 100644
index 28f0f71..0000000
--- a/scripts/reproduce_stsf_results.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-export CUDA_VISIBLE_DEVICES=0
-
-DATA_DIR=./datasets
-LOG_DIR=./exps
-
-CTX_LEN=96
-
-for DATASET in 'solar' 'electricity' 'exchange' 'traffic' 'wiki'
-do
-    for MODEL in 'dlinear' 'patchtst' 'gru_nvp' 'gru_maf' 'trans_maf' 'timegrad' 'csdi' 'timesnet'
-    do
-        python run.py --config config/stsf/${DATASET}/${MODEL}.yaml --seed_everything 0  \
-            --data.data_manager.init_args.path ${DATA_DIR} \
-            --trainer.default_root_dir ${LOG_DIR} \
-            --data.data_manager.init_args.split_val true 
-    done
-done
diff --git a/scripts/reproduce_tsfm_results.sh b/scripts/reproduce_tsfm_results.sh
deleted file mode 100644
index 3a2997a..0000000
--- a/scripts/reproduce_tsfm_results.sh
+++ /dev/null
@@ -1,148 +0,0 @@
-export CUDA_VISIBLE_DEVICES=0
-
-DATA_DIR=./datasets
-LOG_DIR=./exps
-
-# MOIRAI
-MODEL='moirai'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf' 'electricity_ltsf'; do
-    for CTX_LEN in 5000 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}/context_${CTX_LEN}/${DATASET}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN}
-        done
-    done
-done
-
-for DATASET in 'exchange_rate_nips' 'solar_nips' 'electricity_nips'; do
-    for CTX_LEN in 5000 96; do
-        python run.py --config config/tsfm/${MODEL}/context_${CTX_LEN}/${DATASET}.yaml --seed_everything 0  \
-            --data.data_manager.init_args.path ${DATA_DIR} \
-            --trainer.default_root_dir ${LOG_DIR} \
-            --data.data_manager.init_args.dataset ${DATASET} 
-    done
-done
-
-# Chronos
-MODEL='chronos'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf'; do
-    for CTX_LEN in 5000 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --data.test_batch_size 1
-        done
-    done
-done
-
-for DATASET in 'exchange_rate_nips' 'traffic_nips'; do
-    for CTX_LEN in 512 96; do
-        for PRED_LEN in 24; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --data.test_batch_size 1
-        done
-    done
-done
-
-# Lag-Llama
-MODEL='lag_llama'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf'; do
-    for CTX_LEN in 512; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --model.forecaster.init_args.ckpt_path './checkpoints/lag-llama/lag-llama.ckpt' \
-                --data.test_batch_size 1
-        done
-    done
-done
-
-# TimesFM
-MODEL='timesfm'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2'; do
-    for CTX_LEN in 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --data.test_batch_size 64
-        done
-    done
-done
-
-# Timer
-MODEL='timer'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf' 'electricity_ltsf'; do
-    for CTX_LEN in 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --model.forecaster.init_args.ckpt_path './checkpoints/timer/Timer_67M_UTSD_4G.pt' \
-                --data.test_batch_size 64
-        done
-    done
-done
-
-# UniTS
-MODEL='units'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2'; do
-    for CTX_LEN in 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --model.forecaster.init_args.ckpt_path './checkpoints/units/units_x128_pretrain_checkpoint.pth' \
-                --data.test_batch_size 64
-        done
-    done
-done
-
-# ForecastPFN
-MODEL='forecastpfn'
-for DATASET in 'etth1' 'etth2' 'ettm1' 'ettm2' 'weather_ltsf'; do
-    for CTX_LEN in 96; do
-        for PRED_LEN in 24 48 96 192 336 720; do
-            python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
-                --data.data_manager.init_args.path ${DATA_DIR} \
-                --trainer.default_root_dir ${LOG_DIR} \
-                --data.data_manager.init_args.split_val true \
-                --data.data_manager.init_args.dataset ${DATASET} \
-                --data.data_manager.init_args.context_length ${CTX_LEN} \
-                --data.data_manager.init_args.prediction_length ${PRED_LEN} \
-                --model.forecaster.init_args.ckpt_path './checkpoints/ForecastPFN/saved_weights' \
-                --data.test_batch_size 64
-        done
-    done
-done
\ No newline at end of file
diff --git a/scripts/run_ablation.sh b/scripts/run_ablation.sh
new file mode 100644
index 0000000..cfde926
--- /dev/null
+++ b/scripts/run_ablation.sh
@@ -0,0 +1,27 @@
+DATA_DIR=/path/to/datasets
+LOG_DIR=/path/to/log_dir
+
+CTX_LEN=96
+TRAIN_PRED_LEN=720
+VAL_PRED_LEN=720
+TEST_PRED_LEN=24-48-96-192-336-720-1024
+
+# select from ['etth1', 'etth2', 'ettm1', 'ettm2', 'traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'weather_ltsf']
+DATASET='etth1' 
+
+# select from ["elastst_abs_pe", "elastst_learnable_pe", "elastst_trope", 
+#  "elastst_wo_trope", "elastst_wo_mask", "elastst_wo_patch", "elastst_wo_pe", "elastst_random_sample"]
+MODEL=elastst_random_sample
+
+
+python run.py --config config/ablation_study/${MODEL}.yaml --seed_everything 0  \
+    --data.data_manager.init_args.path ${DATA_DIR} \
+    --trainer.default_root_dir ${LOG_DIR} \
+    --data.data_manager.init_args.split_val true \
+    --data.data_manager.init_args.dataset ${DATASET} \
+    --data.data_manager.init_args.context_length ${CTX_LEN} \
+    --data.data_manager.init_args.prediction_length ${TEST_PRED_LEN} \
+    --data.data_manager.init_args.train_pred_len_list ${TRAIN_PRED_LEN} \
+    --data.data_manager.init_args.train_ctx_len_list ${CTX_LEN} \
+    --data.data_manager.init_args.val_pred_len_list $VAL_PRED_LEN \
+    --trainer.max_epochs 50
\ No newline at end of file
diff --git a/scripts/run_baselines.sh b/scripts/run_baselines.sh
new file mode 100644
index 0000000..e269094
--- /dev/null
+++ b/scripts/run_baselines.sh
@@ -0,0 +1,26 @@
+DATA_DIR=/path/to/datasets
+LOG_DIR=/path/to/log_dir
+
+CTX_LEN=96
+
+TRAIN_PRED_LEN=720
+VAL_PRED_LEN=720
+TEST_PRED_LEN=720
+
+# select from ['etth1', 'etth2', 'ettm1', 'ettm2', 'traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'weather_ltsf']
+DATASET='exchange_ltsf' 
+
+# select from ['elastst', 'autoformer', 'dlinear', 'patchtst', 'transformer_enc', 'tsmixer']
+MODEL=tsmixer
+
+python run.py --config config/baselines/${DATASET}/${MODEL}.yaml --seed_everything 0  \
+    --data.data_manager.init_args.path ${DATA_DIR} \
+    --trainer.default_root_dir ${LOG_DIR} \
+    --data.data_manager.init_args.split_val true \
+    --data.data_manager.init_args.dataset ${DATASET} \
+    --data.data_manager.init_args.context_length ${CTX_LEN} \
+    --data.data_manager.init_args.prediction_length ${TEST_PRED_LEN} \
+    --data.data_manager.init_args.train_pred_len_list ${TRAIN_PRED_LEN} \
+    --data.data_manager.init_args.train_ctx_len_list ${CTX_LEN} \
+    --data.data_manager.init_args.val_pred_len_list $VAL_PRED_LEN \
+    --trainer.max_epochs 1 

From 2af961bf81ca04ca7e851e2a5c0256bf94ac5953 Mon Sep 17 00:00:00 2001
From: Jiawen 
Date: Mon, 21 Oct 2024 15:02:33 +0000
Subject: [PATCH 2/3] update readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 318b78a..a81262e 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ElasTST (NeurIPS 2024)
 
-This is an offical implementation of ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer.
+This `elastst` branch is the official implementation of “ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer”. It is designed to reproduce all results reported in the paper.
 
 
 ## Key Designs 💡

From d49f7e41c2db7ac3208816225885b6e3f61c0fb3 Mon Sep 17 00:00:00 2001
From: Jiawen 
Date: Tue, 22 Oct 2024 01:36:22 +0000
Subject: [PATCH 3/3] update shell scripts

---
 run.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/run.sh b/run.sh
index 0377425..298be7c 100644
--- a/run.sh
+++ b/run.sh
@@ -12,7 +12,7 @@ DATASET='exchange_ltsf'
 
 MODEL=elastst
 
-python run.py --config config/ablation_study/${MODEL}.yaml --seed_everything 0  \
+python run.py --config config/${MODEL}.yaml --seed_everything 0  \
     --data.data_manager.init_args.path ${DATA_DIR} \
     --trainer.default_root_dir ${LOG_DIR} \
     --data.data_manager.init_args.split_val true \

arei-@<-
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