docs: def time_reversal_asymmetry_statistic(x, lag):
      """
      This function calculates the value of
  
      .. math::
  
          \\frac{1}{n-2lag} \sum_{i=0}^{n-2lag} x_{i + 2 \cdot lag}^2 \cdot x_{i + lag} - x_{i + lag} \cdot  x_{i}^2
  
      which is
  
      .. math::
  
          \\mathbb{E}[L^2(X)^2 \cdot L(X) - L(X) \cdot X^2]
  
      where :math:`\\mathbb{E}` is the mean and :math:`L` is the lag operator. It was proposed in [1] as a
      promising feature to extract from time series.
  
      .. rubric:: References
  
      |  [1] Fulcher, B.D., Jones, N.S. (2014).
      |  Highly comparative feature-based time-series classification.
      |  Knowledge and Data Engineering, IEEE Transactions on 26, 3026–3037.
  
      :param x: the time series to calculate the feature of
      :type x: numpy.ndarray
      :param lag: the lag that should be used in the calculation of the feature
      :type lag: int
      :return: the value of this feature
      :return type: float
      """
      n = len(x)
      x = np.asarray(x)
      if 2 * lag >= n:
          return 0
      else:
          one_lag = _roll(x, -lag)
          two_lag = _roll(x, 2 * -lag)
          return np.mean((two_lag * two_lag * one_lag - one_lag * x * x)[0:(n - 2 * lag)])