docstr: Unscale the time series dataset's feature column and target column.
  
  :return: the tsdataset instance.
docstr_tok: ["Unscale","the","time","series","dataset","'s","feature","column","and","target","column",".",":","return",":","the","tsdataset","instance","."]
code: def unscale(self):
      """
      Unscale the time series dataset's feature column and target column.
  
      :return: the tsdataset instance.
      """
      feature_col = self.feature_col
      if self.roll_additional_feature:
          feature_col = []
          for feature in self.feature_col:
              if feature not in self.roll_additional_feature:
                  feature_col.append(feature)
      self.df[
          self.target_col + feature_col
      ] = self.scaler.inverse_transform(
          self.df[self.target_col + feature_col]
      )
      return self
  
imports: import pandas
  import numpy
  import functools
  from zoo.chronos.data.utils.feature import generate_dt_features, generate_global_features
  from zoo.chronos.data.utils.impute import impute_timeseries_dataframe
  from zoo.chronos.data.utils.deduplicate import deduplicate_timeseries_dataframe
  from zoo.chronos.data.utils.roll import roll_timeseries_dataframe
  from zoo.chronos.data.utils.scale import unscale_timeseries_numpy
  from zoo.chronos.data.utils.resample import resample_timeseries_dataframe
  from zoo.chronos.data.utils.split import split_timeseries_dataframe
  from zoo.chronos.data.utils.utils import _to_list, _check_type, _check_col_within, _check_col_no_na, _check_is_aligned, _check_dt_is_sorted
  from tsfresh.utilities.dataframe_functions import roll_time_series
  from tsfresh.utilities.dataframe_functions import impute
  from tsfresh import extract_features
  from tsfresh.feature_extraction import ComprehensiveFCParameters, MinimalFCParameters, EfficientFCParameters
label_desc: Use image node_id 15 for calling the TSDataset obj's underlying member method code with example usage: obj.unscale() and returns: self