text: Title: What is the best way to generat time_idx for global tft model with different lenght of timeseries in data?
  Body: - PyTorch-Forecasting version: 0.10.3
  - PyTorch version: 1.13
  - Python version: 3.8
  - Operating System: Ubuntu 18.04
  
  ### Expected behavior
  
  I am going to create a global model to predict demand or each pair of Store and Product. But fir this purpose I have created my own function for time_idx generation. 
  The first function is (creates separate time_idx - different for all time series in data): : 
  
  `
  def time_idx_func(x: pd.DataFrame, config: addict.Dict, data_freq: str="D") -> pd.DataFrame:
      """
      Creates special integer time index for a given dataframe, based on a classic Datetime column. 
      It is necessary for a pytorch-forecasting framework as a requirements. 
  
      Args:
        x (pd.DataFrame):  Input dataframe with datetime column.
        data_freq (str): Data freq for using in pd.date_range function
        config (addict.addict.Dict): 
      
      Returns:
        pd.DataFrame: Result dataframe with integer index column.
      """
      date_range = pd.date_range(
          start = x[config.params.date_column].min(), 
          end = x[config.params.date_column].max(), 
          freq = data_freq
      )
      date_range_df = pd.DataFrame({
          config.params.date_column: date_range
      })
      date_range_df[config.params.date_column] = date_range_df[config.params.date_column].astype(str)
      
      date_range_df['time_idx'] = date_range_df.index
      x = pd.merge(x, date_range_df, on = [config.params.date_column], how = 'left')
      return x
  `
  
  The second functions is (creates global time_idx - same for all time series in data): 
  `
  def time_idx_func(x: pd.DataFrame, config: addict.Dict, data_freq: str="D") -> pd.DataFrame:
      """
      Creates special integer time index for a given dataframe, based on a classic Datetime column. 
      It is necessary for a pytorch-forecasting framework as a requirements. 
  
      Args:
        x (pd.DataFrame):  Input dataframe with datetime column.
        data_freq (str): Data freq for using in pd.date_range function
        config (addict.addict.Dict): 
      
      Returns:
        pd.DataFrame: Result dataframe with integer index column.
      """
      if data_freq == 'D':
          date_range = pd.date_range(
              start = x[config.params.date_column].min(), 
              end = x[config.params.date_column].max(), 
              freq = data_freq
          )
      else:
          date_range = np.sort(x[config.params.date_column].unique())
      
      date_range_df = pd.DataFrame({
          config.params.date_column: date_range
      })
      date_range_df[config.params.date_column] = date_range_df[config.params.date_column].astype(str)
      
      date_range_df['time_idx'] = date_range_df.index
      x = pd.merge(x, date_range_df, on = [config.params.date_column], how = 'left')
      return x
  `
  ### Actual behavior
  
  So can you explain what is the right way to create and pass time idx for TFT?
  
  