code: def approximate_entropy(self, x, m=None, r=None):
          
          if m is None or r is None:
              m = 2
              r = 0.3
          entropy = feature_calculators.approximate_entropy(x, m, r)
          logging.debug("approximate entropy by tsfresh calculated")
          return entropy
docs: As in tsfresh \
          `approximate_entropy <https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/\
          feature_calculators.py#L1601>`_
  
          Implements a `vectorized approximate entropy algorithm <https://en.wikipedia.org/wiki/Approximate_entropy>`_ 
          For short time-series this method is highly dependent on the parameters,
          but should be stable for N > 2000, see :cite:`Yentes2013`. Other shortcomings and alternatives discussed in \
          :cite:`Richman2000`
          
          :param x: the time series to calculate the feature of
          :type x: pandas.Series
          :param m: Length of compared run of data
          :type m: int
          :param r: Filtering level, must be positive
          :type r: float
          :return: Approximate entropy
          :rtype: float