AutoThetaForecaster

yohou_nixtla.stats.AutoThetaForecaster

Bases: BaseStatsForecaster

AutoTheta forecaster via statsforecast.

Automatic selection of the best Theta model.

Parameters

Name	Type	Description	Default
season_length	int	Length of the seasonal period.	1
decomposition_type	str	Type of seasonal decomposition.	"multiplicative"
freq	str or None	Frequency string. Auto-inferred from data if None.	None
feature_transformer	BaseTransformer or None	Transformer applied to exogenous features before fitting/predicting.	None
target_transformer	BaseTransformer or None	Transformer applied to the target before fitting. Inverse-transformed after predicting to return forecasts in the original scale.	None
target_as_feature	('transformed', 'raw')	Whether to include target values as additional features.	"transformed"
**params	dict	Additional parameters forwarded to statsforecast.models.AutoTheta.	{}

Attributes

Name	Type	Description
nixtla_forecaster_	StatsForecast	The fitted Nixtla orchestrator.
instance_	AutoTheta	The constructed AutoTheta model instance.

See Also

ThetaForecaster : Theta with manually specified parameters.

Examples

>>> from yohou_nixtla.stats import AutoThetaForecaster
>>> forecaster = AutoThetaForecaster(season_length=12)
>>> forecaster
AutoThetaForecaster(...)

Source Code

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class AutoThetaForecaster(BaseStatsForecaster):
    """AutoTheta forecaster via statsforecast.

    Automatic selection of the best Theta model.

    Parameters
    ----------
    season_length : int, default=1
        Length of the seasonal period.
    decomposition_type : str, default="multiplicative"
        Type of seasonal decomposition.
    freq : str or None, default=None
        Frequency string. Auto-inferred from data if None.
    feature_transformer : BaseTransformer or None, default=None
        Transformer applied to exogenous features before fitting/predicting.
    target_transformer : BaseTransformer or None, default=None
        Transformer applied to the target before fitting. Inverse-transformed
        after predicting to return forecasts in the original scale.
    target_as_feature : {"transformed", "raw"} or None, default=None
        Whether to include target values as additional features.
    **params : dict
        Additional parameters forwarded to ``statsforecast.models.AutoTheta``.

    Attributes
    ----------
    nixtla_forecaster_ : StatsForecast
        The fitted Nixtla orchestrator.
    instance_ : AutoTheta
        The constructed AutoTheta model instance.

    See Also
    --------
    ThetaForecaster : Theta with manually specified parameters.

    Examples
    --------
    >>> from yohou_nixtla.stats import AutoThetaForecaster
    >>> forecaster = AutoThetaForecaster(season_length=12)
    >>> forecaster  # doctest: +ELLIPSIS
    AutoThetaForecaster(...)

    """

    _estimator_default_class = AutoTheta