code: def add_prediction(self):
          
          self.df = self._predict(fit=self.fit, df=self.df)
docs: Add predictions and confidence interval to self.df
          self.df will contain the following columns:
          - 'predicted': the model output
          - 'interval_u', 'interval_l': upper and lower confidence bounds.
  
          Parameters
          ----------
          None, but the result depends on the following attributes of self:
          confint : float (default=0.95)
              Confidence level for two-sided hypothesis
          allow_negative_predictions : bool (default=True)
              If False, correct negative predictions to zero (typically for energy consumption predictions)
  
          Returns
          -------
          Nothing, adds columns to self.df