Method predictive_samples

python/prophet/forecaster.py:1815–1838

Sample from the posterior predictive distribution. Returns samples for the main estimate yhat, and for the trend component. The shape of each output will be (nforecast x nsamples), where nforecast is the number of points being forecasted (the number of rows in the input

(self, df: pd.DataFrame, vectorized: bool = True)

        return sample_trends - sample_trends.mean(axis=0)

    def predictive_samples(self, df: pd.DataFrame, vectorized: bool = True):
        """Sample from the posterior predictive distribution. Returns samples
        for the main estimate yhat, and for the trend component. The shape of
        each output will be (nforecast x nsamples), where nforecast is the
        number of points being forecasted (the number of rows in the input
        dataframe) and nsamples is the number of posterior samples drawn.
        This is the argument `uncertainty_samples` in the Prophet constructor,
        which defaults to 1000.

        Parameters
        ----------
        df: Dataframe with dates for predictions (column ds), and capacity
            (column cap) if logistic growth.
        vectorized: Whether to use a vectorized method to compute possible draws. Suggest using
            True (the default) for much faster runtimes in most cases,
            except when (growth = 'logistic' and mcmc_samples > 0).

        Returns
        -------
        Dictionary with keys "trend" and "yhat" containing
        posterior predictive samples for that component.
        """
        df = self.setup_dataframe(df.copy())
        return self.sample_posterior_predictive(df, vectorized)

    def percentile(self, a, *args, **kwargs):
        """