statsmodels.tsa.statespace.dynamic_factor.DynamicFactorResults.predict

DynamicFactorResults.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).

See also

forecast

Out-of-sample forecasts.

get_forecast

Out-of-sample forecasts and results including confidence intervals.

get_prediction

In-sample predictions / out-of-sample forecasts and results including confidence intervals.