statsmodels.tsa.exponential_smoothing.ets.ETSResults.get_prediction¶

ETSResults.get_prediction(start=None, end=None, dynamic=False, index=None, method=None, simulate_repetitions=1000, **simulate_kwargs)[source]¶

Calculates mean prediction and prediction intervals.

Parameters:¶

start : int, 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.

end : int, 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.

dynamic : bool, 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.

index : pd.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.

method : str or None, optional¶

Method to use for calculating prediction intervals. ‘exact’ (default, if available) or ‘simulated’.

simulate_repetitions : int, optional¶

Number of simulation repetitions for calculating prediction intervals when method='simulated'. Default is 1000.

**simulate_kwargs¶

Additional arguments passed to the simulate method.

Returns:¶

Predicted mean values and prediction intervals

Return type:¶

PredictionResults