_AutoARIMAAdapter

class _AutoARIMAAdapter(d: Optional[int] = None, D: Optional[int] = None, max_p: int = 5, max_q: int = 5, max_P: int = 2, max_Q: int = 2, max_order: int = 5, max_d: int = 2, max_D: int = 1, start_p: int = 2, start_q: int = 2, start_P: int = 1, start_Q: int = 1, season_length: int = 1, **kwargs)[source]

Bases: etna.models.statsforecast._StatsForecastBaseAdapter

Adapter for statsforecast.models.AutoARIMA.

Init model with given params.

Parameters

  • d (Optional[int]) – Order of first-differencing.

  • D (Optional[int]) – Order of seasonal-differencing.

  • max_p (int) – Max autorregresives p.

  • max_q (int) – Max moving averages q.

  • max_P (int) – Max seasonal autorregresives P.

  • max_Q (int) – Max seasonal moving averages Q.

  • max_order (int) – Max p+q+P+Q value if not stepwise selection.

  • max_d (int) – Max non-seasonal differences.

  • max_D (int) – Max seasonal differences.

  • start_p (int) – Starting value of p in stepwise procedure.

  • start_q (int) – Starting value of q in stepwise procedure.

  • start_P (int) – Starting value of P in stepwise procedure.

  • start_Q (int) – Starting value of Q in stepwise procedure.

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • **kwargs – Additional parameters for statsforecast.models.AutoARIMA.

Inherited-members

Methods

fit(df, regressors)

Fit statsforecast adapter.

forecast(df[, prediction_interval, quantiles])

Compute predictions on future data from a statsforecast model.

forecast_components(df)

Estimate forecast components.

get_model()

Get statsforecast model that is used inside etna class.

predict(df[, prediction_interval, quantiles])

Compute in-sample predictions from a statsforecast model.

predict_components(df)

Estimate prediction components.