Method sample_model_vectorized

python/prophet/forecaster.py:1539–1579

Simulate observations from the extrapolated generative model. Vectorized version of sample_model(). Parameters ---------- df: Prediction dataframe. seasonal_features: pd.DataFrame of seasonal features. iteration: Int sampling iteration to use parameters from.

(
        self,
        df: pd.DataFrame,
        seasonal_features: pd.DataFrame,
        iteration: int,
        s_a: np.ndarray,
        s_m: np.ndarray,
        n_samples: int,
    )

        }

    def sample_model_vectorized(
        self,
        df: pd.DataFrame,
        seasonal_features: pd.DataFrame,
        iteration: int,
        s_a: np.ndarray,
        s_m: np.ndarray,
        n_samples: int,
    ) -> List[Dict[str, np.ndarray]]:
        """Simulate observations from the extrapolated generative model. Vectorized version of sample_model().

        Parameters
        ----------
        df: Prediction dataframe.
        seasonal_features: pd.DataFrame of seasonal features.
        iteration: Int sampling iteration to use parameters from.
        s_a: Indicator vector for additive components.
        s_m: Indicator vector for multiplicative components.
        n_samples: Number of future paths of the trend to simulate.

        Returns
        -------
        List (length n_samples) of dictionaries with arrays for trend and yhat, each ordered like df['t'].
        """
        # Get the seasonality and regressor components, which are deterministic per iteration
        beta = self.params['beta'][iteration]
        Xb_a = np.matmul(seasonal_features.values,
                        beta * s_a.values) * self.y_scale
        Xb_m = np.matmul(seasonal_features.values, beta * s_m.values)
        # Get the future trend, which is stochastic per iteration
        trends = self.sample_predictive_trend_vectorized(df, n_samples, iteration)  # already on the same scale as the actual data
        sigma = self.params['sigma_obs'][iteration]
        noise_terms = np.random.normal(0, sigma, trends.shape) * self.y_scale

        simulations = []
        for trend, noise in zip(trends, noise_terms):
            simulations.append({
                'yhat': trend * (1 + Xb_m) + Xb_a + noise,
                'trend': trend
            })
        return simulations

    def sample_predictive_trend(self, df, iteration):
        """Simulate the trend using the extrapolated generative model.