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
