def predict_is(self, h=5, fit_once=True):
        """ Makes dynamic in-sample predictions with the estimated model

        Parameters
        ----------
        h : int (default : 5)
            How many steps would you like to forecast?

        fit_once : boolean
            (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
            This method is not functional currently for this model

        Returns
        ----------
        - pd.DataFrame with predicted values
        """     

        predictions = []

        for t in range(0,h):
            x = NLLEV(family=self.family, integ=self.integ, data=self.data_original[:(-h+t)])
            x.fit(print_progress=False)
            if t == 0:
                predictions = x.predict(h=1)
            else:
                predictions = pd.concat([predictions,x.predict(h=1)])

        predictions.rename(columns={0:self.data_name}, inplace=True)
        predictions.index = self.index[-h:]

        return predictions
Makes dynamic in-sample predictions with the estimated model

        Parameters
        ----------
        h : int (default : 5)
            How many steps would you like to forecast?

        fit_once : boolean
            (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
            This method is not functional currently for this model

        Returns
        ----------
        - pd.DataFrame with predicted values