code: def shift_dates(self,h):
          
  
          date_index = copy.deepcopy(self.index)
          date_index = date_index[self.max_lag:len(date_index)]
  
          if self.is_pandas is True:
  
              if isinstance(date_index, pd.core.indexes.datetimes.DatetimeIndex):
  
                  if pd.infer_freq(date_index) in ['H', 'M', 'S']:
  
                      for t in range(h):
                          date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).seconds)
  
                  else: # Assume higher frequency (configured for days)
  
                      for t in range(h):
                          date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).days)
  
              elif isinstance(date_index, pd.core.indexes.numeric.Int64Index):
  
                  for i in range(h):
                      new_value = date_index.values[len(date_index.values)-1] + (date_index.values[len(date_index.values)-1] - date_index.values[len(date_index.values)-2])
                      date_index = pd.Int64Index(np.append(date_index.values,new_value))
  
          else:
  
              for t in range(h):
                  date_index.append(date_index[len(date_index)-1]+1)
  
          return date_index
docs: Auxiliary function for creating dates for forecasts
  
          Parameters
          ----------
          h : int
              How many steps to forecast
  
          Returns
          ----------
          A transformed date_index object