StatsForecastAutoETS
StatsForecast Automatic Exponential Smoothing model.
Direct interface to statsforecast.models.AutoETS, from statsforecast [1] by Nixtla. The statsforecast implementation is a mirror of Hyndman’s forecast::ets [2].
Automatically selects the best ETS (Error, Trend, Seasonality) model using an information criterion. Default is Akaike Information Criterion (AICc), while particular models are estimated using maximum likelihood. The state-space equations can be determined based on their $M$ multiplicative, $A$ additive, $Z$ optimized or $N$ omitted components. The model string parameter defines the ETS equations: E in [$M, A, Z$], T in [$N, A, M, Z$], and S in [$N, A, M, Z$].
For example when model=’ANN’ (additive error, no trend, and no seasonality), ETS will explore only a simple exponential smoothing.
If the component is selected as ‘Z’, it operates as a placeholder to ask the AutoETS model to figure out the best parameter.
Schnellstart
from sktime.forecasting.statsforecast import StatsForecastAutoETS
estimator = StatsForecastAutoETS(season_length: int=1, model: str='ZZZ', damped: bool | None=None, phi: float | None=None)Parameter(4)
- season_lengthint, optional (default=1)
- Number of observations per unit of time. Ex: 24 Hourly data.
- modelstr, optional (default=”ZZZ”)
- Controlling state-space-equations.
- dampedbool, optional (default=None)
- A parameter that ‘dampens’ the trend.
- phifloat, optional (default=None)
Smoothing parameter for trend damping. Only used when
damped=True.
Beispiele
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.statsforecast import StatsForecastAutoETS
>>> y = load_airline ()
>>> forecaster = StatsForecastAutoETS (
... season_length = 12, model = "AAN", damped = True
... )
>>> forecaster. fit (y) StatsForecastAutoETS(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])