def approximate_entropy(self, x, m=None, r=None):
        """
        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
        """
        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