Method predict

python/prophet/forecaster.py:1249–1294

Predict using the prophet model. Parameters ---------- df: pd.DataFrame with dates for predictions (column ds), and capacity (column cap) if logistic growth. If not provided, predictions are made on the history. vectorized: Whether to use a ve

(self, df: pd.DataFrame = None, vectorized: bool = True)

        return self

    def predict(self, df: pd.DataFrame = None, vectorized: bool = True) -> pd.DataFrame:
        """Predict using the prophet model.

        Parameters
        ----------
        df: pd.DataFrame with dates for predictions (column ds), and capacity
            (column cap) if logistic growth. If not provided, predictions are
            made on the history.
        vectorized: Whether to use a vectorized method to compute uncertainty intervals. Suggest using
            True (the default) for much faster runtimes in most cases,
            except when (growth = 'logistic' and mcmc_samples > 0).

        Returns
        -------
        A pd.DataFrame with the forecast components.
        """
        if self.history is None:
            raise Exception('Model has not been fit.')

        if df is None:
            df = self.history.copy()
        else:
            if df.shape[0] == 0:
                raise ValueError('Dataframe has no rows.')
            df = self.setup_dataframe(df.copy())

        df['trend'] = self.predict_trend(df)
        seasonal_components = self.predict_seasonal_components(df)
        if self.uncertainty_samples:
            intervals = self.predict_uncertainty(df, vectorized)
        else:
            intervals = None

        # Drop columns except ds, cap, floor, and trend
        cols = ['ds', 'trend']
        if 'cap' in df:
            cols.append('cap')
        if self.logistic_floor:
            cols.append('floor')
        # Add in forecast components
        df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)
        df2['yhat'] = (
                df2['trend'] * (1 + df2['multiplicative_terms'])
                + df2['additive_terms']
        )
        return df2

    @staticmethod
    def piecewise_linear(t, deltas, k, m, changepoint_ts):