code: def change_quantiles(self, x, ql=None, qh=None, isabs=None, f_agg=None):
          
          if ql is None or qh is None or isabs is None or f_agg is None:
              f_agg = 'mean'
              isabs = True
              qh = 0.2
              ql = 0.0
          quantile = feature_calculators.change_quantiles(x, ql, qh, isabs, f_agg)
          logging.debug("change_quantiles by tsfresh calculated")
          return quantile
docs: As in tsfresh `change_quantiles <https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/\
              feature_extraction/feature_calculators.py#L1248>`_
  
              First fixes a corridor given by the quantiles ql and qh of the distribution of x. Then calculates the \
              average, absolute value of consecutive changes of the series x inside this corridor. Think about selecting \
              a corridor on the y-Axis and only calculating the mean of the absolute change of the time series inside \
              this corridor.
              
              
              :param x: the time series to calculate the feature of
              :type x: pandas.Series
              :param ql: the lower quantile of the corridor
              :type ql: float
              :param qh: the higher quantile of the corridor
              :type qh: float
              :param isabs: should the absolute differences be taken?
              :type isabs: bool
              :param f_agg: the aggregator function that is applied to the differences in the bin
              :type f_agg: str, name of a numpy function (e.g. mean, var, std, median)
              :return: the value of this feature
              :rtype: float