def _run_exponential_smoothing_forecast(self, individual: tuple) -> dict: f = Forecast(self.__orders, self.__average_order) simple_expo_smoothing = [] for sm_lvl in individual: p = [i for i in f.simple_exponential_smoothing(sm_lvl)] # print(p) simple_expo_smoothing.append(p) appraised_individual = {} for smoothing_level in individual: sum_squared_error = f.sum_squared_errors_indi( simple_expo_smoothing, smoothing_level) standard_error = f.standard_error(sum_squared_error, len(self.__orders), smoothing_level) appraised_individual.update({smoothing_level: standard_error}) # print('The standard error as a trait has been calculated {}'.format(appraised_individual)) return appraised_individual