This package implements two cross-validation algorithms suitable to evaluate machine learning models based on time series datasets where each sample is tagged with a prediction time and an evaluation time.

• A Medium post providing some motivation and explaining the cross-validation algorithms implemented here in more detail.
• Advances in financial machine learning by Marcos Lopez de Prado. An excellent book that inspired this package.
• Github repository

timeseriescv can be installed using pip:

>>> pip install git+https://github.com/pythagorea1/timeseriescv

For now the package contains two main classes handling cross-validation:

• PurgedWalkForwardCV: Walk-forward cross-validation with purging.
• CombPurgedKFoldCV: Combinatorial cross-validation with purging and embargoing.

>>> from timeseriescv.cross_validation import CombPurgedKFoldCV as CPCV
>>> cpcv = CPCV(n_splits=10, n_test_splits=2)
>>> for (train_set, test_set) in cpcv.split(df):
>>>     train_X = df.iloc[train_set]
>>>     test_X = df.iloc[test_set]

>>> from timeseriescv.walkforward import PurgedWalkForwardCV as WF
>>> wf = WF(n_splits=10, n_test_splits=2, purge_count=10)
>>> for (train_set, test_set) in wf.split(df):
>>>     train_X = df.iloc[train_set]
>>>     test_X = df.iloc[test_set]

The API is as similar to the scikit-learn API as possible. Like the scikit-learn cross-validation classes, the split method is a generator that yields a pair of numpy arrays containing the positional indices of the samples in the train and validation set, respectively. The main differences with the scikit-learn API are:

Check the docstrings of the cross-validation classes for more information.