code: def predict_is(self, h=5, fit_once=True):
               
  
          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
docs: 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