code: def agg_linear_trend(self, x, param=None):
          
          if param is None:
              param = [{'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'min'},
                       {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'var'},
                       {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'min'}]
          agg = feature_calculators.agg_linear_trend(x, param)
          logging.debug("agg linear trend by tsfresh calculated")
          return list(agg)
docs: As in tsfresh `agg_inear_trend <https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/\
              feature_extraction/feature_calculators.py#L1727>`_
              
              Calculates a linear least-squares regression for values of the time series that were aggregated over chunks\
              versus the sequence from 0 up to the number of chunks minus one.
  
              This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model.
  
              The parameters attr controls which of the characteristics are returned. Possible extracted attributes are\
              "pvalue", "rvalue", "intercept", "slope", "stderr", see the documentation of linregress for more \
              information.
  
              The chunksize is regulated by "chunk_len". It specifies how many time series values are in each chunk.
  
              Further, the aggregation function is controlled by "f_agg", which can use "max", "min" or , "mean", "median"
  
              :param x: the time series to calculate the feature of
              :type x: pandas.Series
              :param param: contains dictionaries {"attr": x, "chunk_len": l, "f_agg": f} with x, f a str and l an int
              :type param: list
              :return: the different feature values
              :rtype: pandas.Series