code: def zscore(ts, **kwargs):
      '''Rolling Z-Score statistics.
      The Z-score is more formally known as ``standardised residuals``.
      To calculate the standardised residuals of a data set,
      the average value and the standard deviation of the data value
      have to be estimated.
  
      .. math::
  
          z = \frac{x - \mu(x)}{\sigma(x)}
      '''
      m = ts.rollmean(**kwargs)
      s = ts.rollstddev(**kwargs)
      result = (ts - m)/s
      name = kwargs.get('name', None)
      if name:
          result.name = name
      return resulf zscore(ts, **kwargs):
      '''Rolling Z-Score statistics.
      The Z-score is more formally known as ``standardised residuals``.
      To calculate the standardised residuals of a data set,
      the average value and the standard deviation of the data value
      have to be estimated.
  
      .. math::
  
          z = \frac{x - \mu(x)}{\sigma(x)}
      '''
      m = ts.rollmean(**kwargs)
      s = ts.rollstddev(**kwargs)
      result = (ts - m)/s
      name = kwargs.get('name', None)
      if name:
          result.name = name
      return result
docs: Rolling Z-Score statistics.
      The Z-score is more formally known as ``standardised residuals``.
      To calculate the standardised residuals of a data set,
      the average value and the standard deviation of the data value
      have to be estimated.
  
      .. math::
  
          z = \frac{x - \mu(x)}{\sigma(x)}