HoltWintersForecaster

yohou_nixtla.stats.HoltWintersForecaster

Bases: BaseStatsForecaster

Holt-Winters (triple exponential smoothing) forecaster via statsforecast.

ETS-style model with error, trend, and seasonality components.

Parameters

Name	Type	Description	Default
season_length	int	Length of the seasonal period.	1
error_type	str	Error type: "A" (additive) or "M" (multiplicative).	"A"
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.HoltWinters.	{}

Attributes

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

See Also

AutoETSForecaster : Automatic ETS model selection.

Examples

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

Source Code

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class HoltWintersForecaster(BaseStatsForecaster):
    """Holt-Winters (triple exponential smoothing) forecaster via statsforecast.

    ETS-style model with error, trend, and seasonality components.

    Parameters
    ----------
    season_length : int, default=1
        Length of the seasonal period.
    error_type : str, default="A"
        Error type: ``"A"`` (additive) or ``"M"`` (multiplicative).
    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.HoltWinters``.

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

    See Also
    --------
    AutoETSForecaster : Automatic ETS model selection.

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

    """

    _estimator_default_class = HoltWinters

    _tags = {"supports_exogenous": True}