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Forecaster

StatsForecastADIDA

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

StatsForecast ADIDA (Aggregate-Disaggregate Intermittent Demand Approach) model.

Direct interface to statsforecast.models.ADIDA by Nixtla.

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

Aggregate-Disagregate Intermittent Demand Approach: Uses temporal aggregation to reduce the number of zero observations. Once the data has been aggregated, it uses the optimized SES to generate the forecasts at the new level. It then breaks down the forecast to the original level using equal weights.

ADIDA specializes on sparse or intermittent series are series with very few non-zero observations.They are notoriously hard to forecast, and so, different methods have been developed specifically for them.

Schnellstart

python
from sktime.forecasting.statsforecast import StatsForecastADIDA

estimator = StatsForecastADIDA(prediction_intervals: object | 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(1)

prediction_intervalsConformalIntervals, optional
Information to compute conformal prediction intervals.

Beispiele

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.statsforecast import StatsForecastADIDA
>>> y = load_airline ()
>>> model = StatsForecastADIDA ()
>>> fitted_model = model. fit (y)
>>> y_pred = fitted_model. predict (fh = [1, 2, 3 ])

Referenzen