Module forecasting (2.17.0)

Forcasting models.

Classes

ARIMAPlus

ARIMAPlus(
    *,
    horizon: int = 1000,
    auto_arima: bool = True,
    auto_arima_max_order: typing.Optional[int] = None,
    auto_arima_min_order: typing.Optional[int] = None,
    data_frequency: str = "auto_frequency",
    include_drift: bool = False,
    holiday_region: typing.Optional[str] = None,
    clean_spikes_and_dips: bool = True,
    adjust_step_changes: bool = True,
    forecast_limit_lower_bound: typing.Optional[float] = None,
    forecast_limit_upper_bound: typing.Optional[float] = None,
    time_series_length_fraction: typing.Optional[float] = None,
    min_time_series_length: typing.Optional[int] = None,
    max_time_series_length: typing.Optional[int] = None,
    trend_smoothing_window_size: typing.Optional[int] = None,
    decompose_time_series: bool = True
)

Time Series ARIMA Plus model.

Parameters
Name Description
horizon int, default 1,000

The number of time points to forecast. Default to 1,000, max value 10,000.
auto_arima bool, default True

Determines whether the training process uses auto.ARIMA or not. If True, training automatically finds the best non-seasonal order (that is, the p, d, q tuple) and decides whether or not to include a linear drift term when d is 1.
auto_arima_max_order int or None, default None

The maximum value for the sum of non-seasonal p and q.
auto_arima_min_order int or None, default None

The minimum value for the sum of non-seasonal p and q.
data_frequency str, default "auto_frequency"

The data frequency of the input time series. Possible values are "auto_frequency", "per_minute", "hourly", "daily", "weekly", "monthly", "quarterly", "yearly"
include_drift bool, default False

Determines whether the model should include a linear drift term or not. The drift term is applicable when non-seasonal d is 1.
holiday_region str or None, default None

The geographical region based on which the holiday effect is applied in modeling. By default, holiday effect modeling isn't used. Possible values see https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region.
clean_spikes_and_dips bool, default True

Determines whether or not to perform automatic spikes and dips detection and cleanup in the model training pipeline. The spikes and dips are replaced with local linear interpolated values when they're detected.
adjust_step_changes bool, default True

Determines whether or not to perform automatic step change detection and adjustment in the model training pipeline.
forecast_limit_upper_bound float or None, default None

The upper bound of the forecasting values. When you specify the forecast_limit_upper_bound option, all of the forecast values must be less than the specified value. For example, if you set forecast_limit_upper_bound to 100, then all of the forecast values are less than 100. Also, all values greater than or equal to the forecast_limit_upper_bound value are excluded from modelling. The forecasting limit ensures that forecasts stay within limits.
forecast_limit_lower_bound float or None, default None

The lower bound of the forecasting values where the minimum value allowed is 0. When you specify the forecast_limit_lower_bound option, all of the forecast values must be greater than the specified value. For example, if you set forecast_limit_lower_bound to 0, then all of the forecast values are larger than 0. Also, all values less than or equal to the forecast_limit_lower_bound value are excluded from modelling. The forecasting limit ensures that forecasts stay within limits.
time_series_length_fraction float or None, default None

The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component.
min_time_series_length int or None, default None

The minimum number of time points that are used in modeling the trend component of the time series.
max_time_series_length int or None, default None

The maximum number of time points in a time series that can be used in modeling the trend component of the time series.
trend_smoothing_window_size int or None, default None

The smoothing window size for the trend component.
decompose_time_series bool, default True

Determines whether the separate components of both the history and forecast parts of the time series (such as holiday effect and seasonal components) are saved in the model.