# statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.forecast¶

ExponentialSmoothingResults.forecast(steps=1, signal_only=False, **kwargs)

Out-of-sample forecasts

Parameters:
stepsint, str, or datetime, optional

If an integer, the number of steps to forecast from the end of the sample. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, steps must be an integer. Default is 1.

signal_onlybool, optional

Whether to compute forecasts 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 forecasts of the “signal” $$Z \alpha_t$$ will be returned. Otherwise, the default is for forecasts of $$y_t$$ to be returned.

**kwargs

Additional arguments may required for forecasting beyond the end of the sample. See FilterResults.predict for more details.

Returns:
forecastarray_like

Out-of-sample forecasts (Numpy array or Pandas Series or DataFrame, depending on input and dimensions). Dimensions are (steps x k_endog).

predict
get_forecast
get_prediction