statsmodels.tsa.exponential_smoothing.ets.ETSResults.predict¶

ETSResults.predict(start=None, end=None, dynamic=False, index=None)[source]¶

In-sample prediction and out-of-sample forecasting

Parameters:¶

startint, str, or datetime, optional

Zero-indexed observation number at which to start forecasting, i.e., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the the zeroth observation.

endint, str, or datetime, optional

Zero-indexed observation number at which to end forecasting, i.e., the last forecast is end. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. Default is the last observation in the sample.

dynamicbool, int, str, or datetime, optional

Integer offset relative to start at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead.

indexpd.Index, optional

Optionally an index to associate the predicted results to. If None, an attempt is made to create an index for the predicted results from the model’s index or model’s row labels.

Returns:¶

forecastarray_like or pd.Series.

Array of out of in-sample predictions and / or out-of-sample forecasts. An (npredict,) array. If original data was a pd.Series or DataFrame, a pd.Series is returned.