code: def predict(self, h=5):      
               
  
          if self.latent_variables.estimated is False:
              raise Exception("No latent variables estimated!")
          else:
              # Retrieve data, dates and (transformed) latent variables         
              date_index = self.shift_dates(h)
  
              # Get expected values
              forecasted_values = np.zeros(h)
  
              for value in range(0,h):
                  if value == 0:
                      forecasted_values[value] = self.states[0][-1] + self.states[1][-1]
                  else:
                      forecasted_values[value] = forecasted_values[value-1] + self.states[1][-1]
  
              result = pd.DataFrame(self.link(forecasted_values))
              result.rename(columns={0:self.data_name}, inplace=True)
              result.index = date_index[-h:]
  
              return result
docs: Makes forecast with the estimated model
  
          Parameters
          ----------
          h : int (default : 5)
              How many steps ahead would you like to forecast?
  
          Returns
          ----------
          - pd.DataFrame with predictions