Method make_future_dataframe

python/prophet/forecaster.py:1851–1885

Simulate the trend using the extrapolated generative model. Parameters ---------- periods: Int number of periods to forecast forward. freq: Any valid frequency for pd.date_range, such as 'D' or 'M'. include_history: Boolean to include the historical dates in

(self, periods, freq='D', include_history=True)

        return fn(a, *args, **kwargs)

    def make_future_dataframe(self, periods, freq='D', include_history=True):
        """Simulate the trend using the extrapolated generative model.

        Parameters
        ----------
        periods: Int number of periods to forecast forward.
        freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.
        include_history: Boolean to include the historical dates in the data
            frame for predictions.

        Returns
        -------
        pd.Dataframe that extends forward from the end of self.history for the
        requested number of periods.
        """
        if self.history_dates is None:
            raise Exception('Model has not been fit.')
        if freq is None:
            # taking the tail makes freq inference more reliable
            freq = pd.infer_freq(self.history_dates.tail(5))
            # returns None if inference failed
            if freq is None:
                raise Exception('Unable to infer `freq`')
        last_date = self.history_dates.max()
        dates = pd.date_range(
            start=last_date,
            periods=periods + 1,  # An extra in case we include start
            freq=freq)
        dates = dates[dates > last_date]  # Drop start if equals last_date
        dates = dates[:periods]  # Return correct number of periods

        if include_history:
            dates = np.concatenate((np.array(self.history_dates), dates))

        return pd.DataFrame({'ds': dates})

    def plot(self, fcst, ax=None, uncertainty=True, plot_cap=True,
             xlabel='ds', ylabel='y', figsize=(10, 6), include_legend=False):