vac = temp.add_holidays(dates) ## <-
ad = temp.associate_days(data[data.columns[1]], 'ven')
yd = temp.generate_days(data[data.columns[1]], 'ven')
anglesd = np.array([temp.convert_day_to_angle(v) for v in yd]) ## <-
ora = np.sin(np.array(data[data.columns[1]]) * np.pi / 24) ## <-
arg = {'holiday': vac, 'day': anglesd, 'ora': ora}
arg = pd.DataFrame(arg)
arfit = statsmodels.tsa.arima_model.ARIMA(endog=ardata["PUN"],
order=[4, 1, 2],
exog=arg.as_matrix()).fit(
trend='c',
method='mle',
maxiter=100)
rmse_fit = Functions_for_TSP.RMSE(arfit.resid) ## 7.7520042757584031
trainset = list(range(8016))
testset = list(range(8016, 8760))
artfit = statsmodels.tsa.arima_model.ARIMA(
endog=ardata["PUN"].ix[trainset],
order=[4, 1, 2],
exog=arg.ix[trainset].as_matrix()).fit(trend='c',
method='mle',
maxiter=100)
art_forecast = artfit.forecast(steps=744, exog=arg.ix[testset].as_matrix())
### http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.arima_model.ARMAResults.html ####
#RMSE(art_forecast[1]-ardata["PUN"].ix[testset])
