Module forecasting (1.11.0)

Version 1.11.0

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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,
    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, defalut 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.
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.