Method fit

python/prophet/forecaster.py:1191–1247

Fit the Prophet model. This sets self.params to contain the fitted model parameters. It is a dictionary parameter names as keys and the following items: k (Mx1 array): M posterior samples of the initial slope. m (Mx1 array): The initial intercept.

(self, df, **kwargs)

        )

    def fit(self, df, **kwargs):
        """Fit the Prophet model.

        This sets self.params to contain the fitted model parameters. It is a
        dictionary parameter names as keys and the following items:
            k (Mx1 array): M posterior samples of the initial slope.
            m (Mx1 array): The initial intercept.
            delta (MxN array): The slope change at each of N changepoints.
            beta (MxK matrix): Coefficients for K seasonality features.
            sigma_obs (Mx1 array): Noise level.
        Note that M=1 if MAP estimation.

        Parameters
        ----------
        df: pd.DataFrame containing the history. Must have columns ds (date
            type) and y, the time series. If self.growth is 'logistic', then
            df must also have a column cap that specifies the capacity at
            each ds.
        kwargs: Additional arguments passed to the optimizing or sampling
            functions in Stan.

        Returns
        -------
        The fitted Prophet object.
        """
        if self.history is not None:
            raise Exception('Prophet object can only be fit once. '
                            'Instantiate a new object.')

        model_inputs = self.preprocess(df, **kwargs)
        initial_params = self.calculate_initial_params(model_inputs.K)

        dat = dataclasses.asdict(model_inputs)
        stan_init = dataclasses.asdict(initial_params)

        if self.history['y'].min() == self.history['y'].max() and \
                (self.growth == 'linear' or self.growth == 'flat'):
            self.params = stan_init
            self.params['sigma_obs'] = 1e-9
            for par in self.params:
                self.params[par] = np.array([self.params[par]])
        elif self.mcmc_samples > 0:
            self.params = self.stan_backend.sampling(stan_init, dat, self.mcmc_samples, **kwargs)
        else:
            self.params = self.stan_backend.fit(stan_init, dat, **kwargs)

        self.stan_fit = self.stan_backend.stan_fit
        # If no changepoints were requested, replace delta with 0s
        if len(self.changepoints) == 0:
            # Fold delta into the base rate k
            self.params['k'] = (
                self.params['k'] + self.params['delta'].reshape(-1)
            )
            self.params['delta'] = (np.zeros(self.params['delta'].shape)
                                      .reshape((-1, 1)))

        return self