code: def predict(self, timeseriesX, n, m):
          
          new_entries = []
          for entry in timeseriesX:
              predicted_value = m * entry[1] + n
              new_entries.append([entry[0], predicted_value])
          return TimeSeries.from_twodim_list(new_entries)
docs: Calculates the dependent timeseries Y for the given parameters
          and independent timeseries. (y=m*x + n)
  
          :param TimeSeries timeseriesX: the independent Timeseries.
  
          :param float n: The interception with the x access
              that has been calculated during regression
  
          :param float m: The slope of the function
              that has been calculated during regression
  
          :return TimeSeries timeseries_y: the predicted values for the
              dependent TimeSeries. Its length and first dimension will
              equal to timeseriesX.