Mean values for standardization: 16.570941929584 -0.000000000000 -0.000000000000 16.570943834781 7.600165752172 0.001731824417 0.000062871513 -0.000023612286 -0.001371538874 1.817635361524 0.000003102750 Standard deviations for standardization: 8.243115956205 0.707126989723 0.707126989723 8.005578068987 2.521753876428 2.955142364061 0.972324960670 0.707611533842 0.706640781853 0.952162633053 0.657176655808 Model for 1 hour(s) forecast: Ridge parameter (h): 0.000009 BIC: 0.007863 Training metrics: R-squared: 0.992185 RMSE: 0.088400 MAE: 0.070176 Validation metrics: R-squared: 0.992274 RMSE: 0.091000 MAE: 0.071396 Coefficients: Intercept = -0.000001 b(1) = -0.066806 b(2) = -0.104624 b(3) = 0.946661 b(4) = -0.019752 b(5) = 0.041678 b(6) = -0.048295 b(7) = -0.009357 b(8) = -0.020131 b(9) = 0.029408 b(10) = 0.155854 VIF: VIF(1) = 1.5706 VIF(2) = 2.2867 VIF(3) = 9.9995 VIF(4) = 2.1403 VIF(5) = 1.3111 VIF(6) = 1.0642 VIF(7) = 2.8821 VIF(8) = 8.3608 VIF(9) = 2.4643 VIF(10) = 2.7155 Model for 2 hour(s) forecast: Ridge parameter (h): 0.000017 BIC: 0.015398 Training metrics: R-squared: 0.984693 RMSE: 0.123711 MAE: 0.097923 Validation metrics: R-squared: 0.984713 RMSE: 0.128002 MAE: 0.100784 Coefficients: Intercept = 0.000001 b(1) = -0.081265 b(2) = -0.107936 b(3) = 0.950668 b(4) = -0.029222 b(5) = 0.040810 b(6) = 0.002134 b(7) = -0.008470 b(8) = -0.017322 b(9) = 0.048945 b(10) = 0.078855 VIF: VIF(1) = 1.5705 VIF(2) = 2.2864 VIF(3) = 9.9960 VIF(4) = 2.1400 VIF(5) = 1.3111 VIF(6) = 1.0642 VIF(7) = 2.8816 VIF(8) = 8.3574 VIF(9) = 2.4641 VIF(10) = 2.7152 Model for 3 hour(s) forecast: Ridge parameter (h): 0.000019 BIC: 0.016077 Training metrics: R-squared: 0.984017 RMSE: 0.126406 MAE: 0.099909 Validation metrics: R-squared: 0.984097 RMSE: 0.130557 MAE: 0.102351 Coefficients: Intercept = 0.000002 b(1) = -0.077926 b(2) = -0.119350 b(3) = 0.943408 b(4) = -0.029416 b(5) = 0.044941 b(6) = -0.002798 b(7) = -0.011178 b(8) = -0.023271 b(9) = 0.048385 b(10) = -0.022327 VIF: VIF(1) = 1.5705 VIF(2) = 2.2862 VIF(3) = 9.9953 VIF(4) = 2.1400 VIF(5) = 1.3112 VIF(6) = 1.0642 VIF(7) = 2.8817 VIF(8) = 8.3563 VIF(9) = 2.4641 VIF(10) = 2.7151 Model for 4 hour(s) forecast: Ridge parameter (h): 0.000013 BIC: 0.011173 Training metrics: R-squared: 0.988892 RMSE: 0.105381 MAE: 0.083031 Validation metrics: R-squared: 0.989068 RMSE: 0.108248 MAE: 0.084538 Coefficients: Intercept = 0.000002 b(1) = -0.067495 b(2) = -0.117758 b(3) = 0.940186 b(4) = -0.022027 b(5) = 0.046064 b(6) = -0.005407 b(7) = -0.011910 b(8) = -0.025537 b(9) = 0.033127 b(10) = -0.105446 VIF: VIF(1) = 1.5705 VIF(2) = 2.2862 VIF(3) = 9.9973 VIF(4) = 2.1401 VIF(5) = 1.3113 VIF(6) = 1.0642 VIF(7) = 2.8824 VIF(8) = 8.3574 VIF(9) = 2.4642 VIF(10) = 2.7152 Model for 5 hour(s) forecast: Ridge parameter (h): 0.000007 BIC: 0.005882 Training metrics: R-squared: 0.994152 RMSE: 0.076462 MAE: 0.059863 Validation metrics: R-squared: 0.994287 RMSE: 0.078252 MAE: 0.060951 Coefficients: Intercept = 0.000011 b(1) = -0.052606 b(2) = -0.091128 b(3) = 0.947855 b(4) = -0.008849 b(5) = 0.039109 b(6) = -0.005520 b(7) = -0.008230 b(8) = -0.018607 b(9) = 0.007750 b(10) = -0.148209 VIF: VIF(1) = 1.5705 VIF(2) = 2.2863 VIF(3) = 9.9997 VIF(4) = 2.1402 VIF(5) = 1.3114 VIF(6) = 1.0642 VIF(7) = 2.8832 VIF(8) = 8.3590 VIF(9) = 2.4643 VIF(10) = 2.7155 Model for 6 hour(s) forecast: Ridge parameter (h): 0.000006 BIC: 0.006123 Training metrics: R-squared: 0.993912 RMSE: 0.078009 MAE: 0.059300 Validation metrics: R-squared: 0.993913 RMSE: 0.080778 MAE: 0.061027 Coefficients: Intercept = 0.000019 b(1) = -0.035761 b(2) = -0.031541 b(3) = 0.970675 b(4) = 0.007846 b(5) = 0.020301 b(6) = -0.000650 b(7) = 0.001205 b(8) = 0.000781 b(9) = -0.021952 b(10) = -0.134354 VIF: VIF(1) = 1.5704 VIF(2) = 2.2863 VIF(3) = 9.9999 VIF(4) = 2.1403 VIF(5) = 1.3116 VIF(6) = 1.0643 VIF(7) = 2.8836 VIF(8) = 8.3587 VIF(9) = 2.4644 VIF(10) = 2.7158 Model for 7 hour(s) forecast: Ridge parameter (h): 0.000009 BIC: 0.009128 Training metrics: R-squared: 0.990924 RMSE: 0.095247 MAE: 0.073131 Validation metrics: R-squared: 0.990793 RMSE: 0.099345 MAE: 0.075406 Coefficients: Intercept = 0.000020 b(1) = -0.007768 b(2) = 0.034879 b(3) = 0.992299 b(4) = 0.020742 b(5) = -0.003796 b(6) = 0.003712 b(7) = 0.009702 b(8) = 0.018743 b(9) = -0.042343 b(10) = -0.099668 VIF: VIF(1) = 1.5703 VIF(2) = 2.2864 VIF(3) = 9.9990 VIF(4) = 2.1402 VIF(5) = 1.3117 VIF(6) = 1.0643 VIF(7) = 2.8837 VIF(8) = 8.3576 VIF(9) = 2.4643 VIF(10) = 2.7161 Model for 8 hour(s) forecast: Ridge parameter (h): 0.000012 BIC: 0.013812 Training metrics: R-squared: 0.986266 RMSE: 0.117166 MAE: 0.091668 Validation metrics: R-squared: 0.986148 RMSE: 0.121862 MAE: 0.093853 Coefficients: Intercept = 0.000002 b(1) = 0.031314 b(2) = 0.099660 b(3) = 1.008249 b(4) = 0.027629 b(5) = -0.030743 b(6) = 0.007946 b(7) = 0.015395 b(8) = 0.031469 b(9) = -0.049255 b(10) = -0.052717 VIF: VIF(1) = 1.5703 VIF(2) = 2.2866 VIF(3) = 9.9972 VIF(4) = 2.1401 VIF(5) = 1.3116 VIF(6) = 1.0643 VIF(7) = 2.8837 VIF(8) = 8.3560 VIF(9) = 2.4642 VIF(10) = 2.7165 Model for 9 hour(s) forecast: Ridge parameter (h): 0.000016 BIC: 0.018939 Training metrics: R-squared: 0.981169 RMSE: 0.137197 MAE: 0.108141 Validation metrics: R-squared: 0.981287 RMSE: 0.141641 MAE: 0.109800 Coefficients: Intercept = -0.000026 b(1) = 0.080412 b(2) = 0.153380 b(3) = 1.014302 b(4) = 0.027613 b(5) = -0.057722 b(6) = 0.010130 b(7) = 0.016673 b(8) = 0.035523 b(9) = -0.041703 b(10) = -0.004305 VIF: VIF(1) = 1.5701 VIF(2) = 2.2866 VIF(3) = 9.9950 VIF(4) = 2.1401 VIF(5) = 1.3116 VIF(6) = 1.0642 VIF(7) = 2.8836 VIF(8) = 8.3542 VIF(9) = 2.4642 VIF(10) = 2.7166 Model for 10 hour(s) forecast: Ridge parameter (h): 0.000020 BIC: 0.023850 Training metrics: R-squared: 0.976286 RMSE: 0.153962 MAE: 0.121109 Validation metrics: R-squared: 0.976744 RMSE: 0.157910 MAE: 0.122491 Coefficients: Intercept = -0.000061 b(1) = 0.136195 b(2) = 0.188056 b(3) = 1.007904 b(4) = 0.021886 b(5) = -0.081876 b(6) = 0.010798 b(7) = 0.012679 b(8) = 0.028943 b(9) = -0.022797 b(10) = 0.036588 VIF: VIF(1) = 1.5700 VIF(2) = 2.2865 VIF(3) = 9.9925 VIF(4) = 2.1401 VIF(5) = 1.3115 VIF(6) = 1.0641 VIF(7) = 2.8835 VIF(8) = 8.3522 VIF(9) = 2.4642 VIF(10) = 2.7163 Model for 11 hour(s) forecast: Ridge parameter (h): 0.000026 BIC: 0.028728 Training metrics: R-squared: 0.971437 RMSE: 0.168974 MAE: 0.132549 Validation metrics: R-squared: 0.972097 RMSE: 0.172972 MAE: 0.133915 Coefficients: Intercept = -0.000084 b(1) = 0.193817 b(2) = 0.197797 b(3) = 0.988343 b(4) = 0.013807 b(5) = -0.101252 b(6) = 0.008704 b(7) = 0.003326 b(8) = 0.011350 b(9) = 0.000657 b(10) = 0.062902 VIF: VIF(1) = 1.5699 VIF(2) = 2.2862 VIF(3) = 9.9893 VIF(4) = 2.1402 VIF(5) = 1.3114 VIF(6) = 1.0641 VIF(7) = 2.8832 VIF(8) = 8.3496 VIF(9) = 2.4643 VIF(10) = 2.7159 Model for 12 hour(s) forecast: Ridge parameter (h): 0.000034 BIC: 0.034266 Training metrics: R-squared: 0.965930 RMSE: 0.184543 MAE: 0.144814 Validation metrics: R-squared: 0.966563 RMSE: 0.189354 MAE: 0.146616 Coefficients: Intercept = -0.000083 b(1) = 0.246006 b(2) = 0.181283 b(3) = 0.957805 b(4) = 0.008730 b(5) = -0.114586 b(6) = 0.007312 b(7) = -0.010365 b(8) = -0.015241 b(9) = 0.018726 b(10) = 0.073852 VIF: VIF(1) = 1.5699 VIF(2) = 2.2859 VIF(3) = 9.9846 VIF(4) = 2.1401 VIF(5) = 1.3114 VIF(6) = 1.0642 VIF(7) = 2.8826 VIF(8) = 8.3461 VIF(9) = 2.4641 VIF(10) = 2.7157