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Forecaster

StatsForecastAutoTheta

API-ReferenzQuellcode
In-Sample-VorhersagenPrognoseintervalleIn-Sample-Prognoseintervalle

Statsforecast AutoTheta estimator.

Direct interface to statsforecast.models.AutoTheta by Nixtla.

This estimator directly interfaces AutoTheta, from statsforecast [1] by Nixtla.

AutoTheta model automatically selects the best Theta (Standard Theta Model (“STM”), Optimized Theta Model (“OTM”), Dynamic Standard Theta Model (“DSTM”), Dynamic Optimized Theta Model (“DOTM”)) model using mse.

Schnellstart

python
from sktime.forecasting.statsforecast import StatsForecastAutoTheta

estimator = StatsForecastAutoTheta(season_length: int=1, decomposition_type: str='multiplicative', model: str | None=None)

Tags

Fähigkeiten

  • In-Sample-Vorhersagen: Unterstützt
  • Prognoseintervalle
  • In-Sample-Prognoseintervalle: Unterstützt
  • Kategoriale Merkmale: Nicht unterstützt
  • Fehlende Werte: Nicht unterstützt
  • Exogene Variablen: Nicht unterstützt
  • Multivariat: Nicht unterstützt

Eigenschaften

Prognosehorizont beim Fit nötigrequires-fh-in-fit
Nein
X und y brauchen denselben IndexX-y-must-have-same-index
Ja

Parameter(3)

season_lengthint, optional, default=1
number of observations per unit of time (e.g. 24 for hourly data), by default 1
decomposition_typestr, optional, default=”multiplicative”
possible values: “additive”, “multiplicative” type of seasonal decomposition, by default “multiplicative”
modelOptional[str], optional
controlling Theta Model, by default searches the best model possible values: “STM”, “OTM”, “DSTM”, “DOTM”

Beispiele

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.statsforecast import StatsForecastAutoTheta
>>> y = load_airline ()
>>> forecaster = StatsForecastAutoTheta (
... season_length = 12, decomposition_type = "additive", model = "OTM"
... )
>>> forecaster. fit (y) StatsForecastAutoTheta(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

Referenzen