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

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

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

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

        predictions = []

        for t in range(0,h):
            data1 = self.data_original_nondf[:-h+t]
            x = DAR(data=data1, ar=self.ar, integ=self.integ)
            if fit_once is False:
                x.fit(printer=False)
            if t == 0:
                if fit_once is True:
                    x.fit(printer=False)
                    saved_lvs = x.latent_variables
                predictions = x.predict(1)
            else:
                if fit_once is True:
                    x.latent_variables = saved_lvs
                predictions = pd.concat([predictions,x.predict(1)])

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

        return predictions