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])