statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.predict¶

ExponentialSmoothingResults.predict(start=None, end=None, dynamic=False, information_set='predicted', signal_only=False, **kwargs)¶

In-sample prediction and out-of-sample forecasting

Parameters:¶

start{int, str,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 zeroth observation.
end{int, str,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,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.
information_setstr, optional: The information set to condition each prediction on. Default is “predicted”, which computes predictions of period t values conditional on observed data through period t-1; these are one-step-ahead predictions, and correspond with the typical fittedvalues results attribute. Alternatives are “filtered”, which computes predictions of period t values conditional on observed data through period t, and “smoothed”, which computes predictions of period t values conditional on the entire dataset (including also future observations t+1, t+2, …).
signal_onlybool, optional: Whether to compute predictions of only the “signal” component of the observation equation. Default is False. For example, the observation equation of a time-invariant model is \(y_t = d + Z \alpha_t + \varepsilon_t\), and the “signal” component is then \(Z \alpha_t\). If this argument is set to True, then predictions of the “signal” \(Z \alpha_t\) will be returned. Otherwise, the default is for predictions of \(y_t\) to be returned.
**kwargs: Additional arguments may be required for forecasting beyond the end of the sample. See FilterResults.predict for more details.

Returns:¶

predictionsarray_like: In-sample predictions / Out-of-sample forecasts. (Numpy array or Pandas Series or DataFrame, depending on input and dimensions). Dimensions are (npredict x k_endog).