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StatsForecastAutoETS

API referenceSource
Categorical in XInsamplePred intPred int insample

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.

Quickstart

python
from sktime.forecasting.statsforecast import StatsForecastAutoETS

estimator = StatsForecastAutoETS(season_length: int=1, model: str='ZZZ', damped: bool | None=None, phi: float | None=None)

Parameters(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.

References