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StatsForecastAutoCES

API referenceSource
Categorical in XInsamplePred intPred int insample

StatsForecast Complex Exponential Smoothing model.

Direct interface to statsforecast.models.AutoCES, from statsforecast [1] by Nixtla.

Automatically selects the best Complex Exponential Smoothing 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 $S$ simple, $P$ partial, $Z$ optimized or $N$ omitted components. The model string parameter defines the kind of CES model: $N$ for simple CES (without seasonality), $S$ for simple seasonality (lagged CES), $P$ for partial seasonality (without complex part), $F$ for full seasonality (lagged CES with real and complex seasonal parts).

If the component is selected as ‘Z’, it operates as a placeholder to ask the AutoCES model to figure out the best parameter.

Quickstart

python
from sktime.forecasting.statsforecast import StatsForecastAutoCES

estimator = StatsForecastAutoCES(season_length: int=1, model: str='Z')

Parameters(2)

season_lengthint, optional (default=1)
Number of observations per unit of time. Ex: 24 Hourly data.
modelstr, optional (default=”Z”)
Controlling state-space-equations.

References