docs: def linear_trend_timewise(x, param):
      """
      Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to
      length of the time series minus one.
      This feature uses the index of the time series to fit the model, which must be of a datetime
      dtype.
      The parameters control which of the characteristics are returned.
  
      Possible extracted attributes are "pvalue", "rvalue", "intercept", "slope", "stderr", see the documentation of
      linregress for more information.
  
      :param x: the time series to calculate the feature of. The index must be datetime.
      :type x: pandas.Series
      :param param: contains dictionaries {"attr": x} with x an string, the attribute name of the regression model
      :type param: list
      :return: the different feature values
      :return type: list
      """
      ix = x.index
  
      # Get differences between each timestamp and the first timestamp in seconds.
      # Then convert to hours and reshape for linear regression
      times_seconds = (ix - ix[0]).total_seconds()
      times_hours = np.asarray(times_seconds / float(3600))
  
      linReg = linregress(times_hours, x.values)
  
      return [("attr_\"{}\"".format(config["attr"]), getattr(linReg, config["attr"]))
              for config in param]