def forecast()

in sa-dsml-many-models/code/util/timeseries_utilities.py [0:0]

• 13 lines of code
• 4 McCabe index (conditional complexity)

    def forecast(self, X):
        """
        Make forecasts over the prediction frame, X.
        X can contain in-sample and out-of-sample data.
        For out-of-sample data, the 1-step-ahead model is recursively applied.
        Returns forecasts for the target in a pd.Series object with the same time index as X.
        np.nan values will be returned for dates where a forecast could not be found.
        """
        assert list(X.index.names) == [self.time_column_name], \
            "Expected time column to comprise input dataframe index."
        # Get in-sample forecasts if requested
        X_insamp = X[X.index <= self._latest_training_date]
        forecasts_insamp = pd.Series()
        if len(X_insamp) > 0:
            forecasts_insamp = self.pipeline.predict(X_insamp)

        # Get out-of-sample forecasts
        X_fcst = X[X.index > self._latest_training_date]
        forecasts = pd.Series()
        if len(X_fcst) > 0:
            # Need to iterate/recurse 1-step forecasts here
            forecasts = self._recursive_forecast(X_fcst)
        forecasts = pd.concat((forecasts_insamp, forecasts))

        return forecasts.reindex(X.index)