ARIMAForecaster

yohou_nixtla.stats.ARIMAForecaster

Bases: BaseStatsForecaster

ARIMA forecaster via statsforecast.

ARIMA model with manually specified orders.

Parameters

Name	Type	Description	Default
order	tuple	The (p, d, q) order of the model.	(0, 0, 0)
season_length	int	Length of the seasonal period. 0 means non-seasonal.	0
seasonal_order	tuple	The (P, D, Q) seasonal order.	(0, 0, 0)
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.ARIMA.	{}

Attributes

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

See Also

AutoARIMAForecaster : Automatic ARIMA order selection.

Examples

>>> from yohou_nixtla.stats import ARIMAForecaster
>>> forecaster = ARIMAForecaster(order=(1, 1, 1))
>>> forecaster
ARIMAForecaster(...)

Source Code

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class ARIMAForecaster(BaseStatsForecaster):
    """ARIMA forecaster via statsforecast.

    ARIMA model with manually specified orders.

    Parameters
    ----------
    order : tuple, default=(0, 0, 0)
        The ``(p, d, q)`` order of the model.
    season_length : int, default=0
        Length of the seasonal period. 0 means non-seasonal.
    seasonal_order : tuple, default=(0, 0, 0)
        The ``(P, D, Q)`` seasonal order.
    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.ARIMA``.

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

    See Also
    --------
    AutoARIMAForecaster : Automatic ARIMA order selection.

    Examples
    --------
    >>> from yohou_nixtla.stats import ARIMAForecaster
    >>> forecaster = ARIMAForecaster(order=(1, 1, 1))
    >>> forecaster  # doctest: +ELLIPSIS
    ARIMAForecaster(...)

    """

    _estimator_default_class = ARIMA

    _tags = {"requires_exogenous": True, "supports_exogenous": True}