With GLMNET
(res1 <- ahead::mlf(AirPassengers, h=25L, lags=20L, fit_func=glmnet::cv.glmnet, stack=FALSE))
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## Point Forecast Lo 95 Hi 95
## Jan 1961 463.0391 357.3187 650.9749
## Feb 1961 429.3018 329.7785 591.0441
## Mar 1961 454.3856 358.5511 633.0725
## Apr 1961 496.4024 395.5703 699.7451
## May 1961 515.6221 411.2922 684.9819
## Jun 1961 567.7786 458.7854 757.9926
## Jul 1961 655.1400 561.1282 840.8359
## Aug 1961 643.5333 547.5059 813.0847
## Sep 1961 550.8073 448.2476 739.6165
## Oct 1961 507.3515 409.0875 679.8325
## Nov 1961 430.9266 329.2196 603.4163
## Dec 1961 468.6047 355.6687 649.3984
## Jan 1962 493.7983 389.9424 671.5721
## Feb 1962 467.8115 363.5454 654.6042
## Mar 1962 487.9759 382.7550 632.0172
## Apr 1962 528.4241 434.0450 684.2442
## May 1962 536.0371 443.2239 684.9515
## Jun 1962 591.0947 495.9845 775.7424
## Jul 1962 683.4788 591.2045 831.6657
## Aug 1962 673.6049 571.1309 834.4357
## Sep 1962 590.9378 490.8353 776.9597
## Oct 1962 537.2731 437.7408 701.5023
## Nov 1962 470.4214 378.4163 648.3351
## Dec 1962 501.3998 408.3683 705.2050
## Jan 1963 524.2541 416.5583 718.8738
(res2 <- ahead::mlf(AirPassengers, h=25L, lags=20L, fit_func=glmnet::cv.glmnet, stack=TRUE))
## Point Forecast Lo 95 Hi 95
## Jan 1961 462.1784 356.4581 650.1143
## Feb 1961 428.8123 329.2889 590.5546
## Mar 1961 453.4933 357.6588 632.1802
## Apr 1961 493.6558 392.8238 696.9985
## May 1961 511.6067 407.2768 680.9664
## Jun 1961 561.4407 452.4474 751.6546
## Jul 1961 644.7154 550.7035 830.4112
## Aug 1961 631.6877 535.6603 801.2391
## Sep 1961 542.1956 439.6359 731.0048
## Oct 1961 502.2619 403.9979 674.7429
## Nov 1961 429.2732 327.5662 601.7629
## Dec 1961 467.1990 354.2631 647.9928
## Jan 1962 490.2826 386.4268 668.0565
## Feb 1962 464.8250 360.5589 651.6177
## Mar 1962 484.2805 379.0596 628.3219
## Apr 1962 521.2972 426.9180 677.1173
## May 1962 526.3959 433.5826 675.3102
## Jun 1962 577.0634 481.9532 761.7111
## Jul 1962 661.7250 569.4507 809.9119
## Aug 1962 648.8165 546.3426 809.6474
## Sep 1962 571.8927 471.7902 757.9146
## Oct 1962 524.9935 425.4612 689.2227
## Nov 1962 464.8087 372.8035 642.7223
## Dec 1962 496.6164 403.5850 700.4216
## Jan 1963 516.3784 408.6826 710.9981
(res3 <- ahead::mlf(USAccDeaths, h=25L, lags=20L, fit_func=glmnet::cv.glmnet, stack=TRUE))
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Point Forecast Lo 95 Hi 95
## Jan 1979 8323.639 6882.876 10026.521
## Feb 1979 7664.944 6263.544 9237.647
## Mar 1979 8187.086 6791.854 10009.931
## Apr 1979 8483.417 7019.539 10051.715
## May 1979 9108.733 7743.821 10959.591
## Jun 1979 9430.902 7900.121 11120.582
## Jul 1979 9984.074 8514.228 11664.142
## Aug 1979 9665.726 8336.470 11328.246
## Sep 1979 9130.261 7646.782 10884.711
## Oct 1979 9077.638 7453.339 10581.203
## Nov 1979 8785.222 7350.128 10238.903
## Dec 1979 9284.110 7646.578 10988.609
## Jan 1980 8606.331 7419.172 10240.486
## Feb 1980 8182.235 6679.172 9988.709
## Mar 1980 8623.021 7243.542 10441.692
## Apr 1980 8705.867 7150.677 10609.309
## May 1980 9183.554 7584.998 10740.264
## Jun 1980 9388.857 7799.638 11126.650
## Jul 1980 9834.055 8796.759 11611.276
## Aug 1980 9545.216 7904.526 11329.144
## Sep 1980 9128.334 7561.849 10930.996
## Oct 1980 9121.847 7693.469 10673.396
## Nov 1980 9084.027 7459.052 10640.910
## Dec 1980 9168.097 7674.100 10747.563
## Jan 1981 8719.799 7173.668 10280.083
(res4 <- ahead::mlf(USAccDeaths, h=25L, lags=20L, fit_func=glmnet::cv.glmnet, stack=FALSE))
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Point Forecast Lo 95 Hi 95
## Jan 1979 8152.003 6711.240 9854.885
## Feb 1979 7134.110 5732.710 8706.813
## Mar 1979 7859.382 6464.150 9682.227
## Apr 1979 8375.888 6912.010 9944.186
## May 1979 9120.707 7755.794 10971.564
## Jun 1979 9604.118 8073.336 11293.798
## Jul 1979 10288.515 8818.669 11968.583
## Aug 1979 10028.789 8699.533 11691.309
## Sep 1979 9455.315 7971.836 11209.766
## Oct 1979 9266.882 7642.582 10770.447
## Nov 1979 8854.911 7419.817 10308.592
## Dec 1979 9321.250 7683.718 11025.749
## Jan 1980 8351.380 7164.221 9985.535
## Feb 1980 7359.233 5856.170 9165.707
## Mar 1980 8057.065 6677.586 9875.737
## Apr 1980 8484.311 6929.120 10387.753
## May 1980 9197.056 7598.499 10753.765
## Jun 1980 9659.976 8070.757 11397.769
## Jul 1980 10287.126 9249.830 12064.347
## Aug 1980 10141.678 8500.988 11925.605
## Sep 1980 9712.442 8145.957 11515.104
## Oct 1980 9481.974 8053.597 11033.524
## Nov 1980 9232.310 7607.335 10789.194
## Dec 1980 9245.665 7751.668 10825.131
## Jan 1981 8434.408 6888.277 9994.692
AirPassengers forecasting plot
par(mfrow=c(1, 2))
plot(res1, main="Conformal ML without stacking")
plot(res2, main="Conformal ML with stacking")
USAccDeaths forecasting plot
par(mfrow=c(1, 2))
plot(res3, main="Conformal ML with stacking")
plot(res4, main="Conformal ML without stacking")
