Computes forecast combination weights using ordinary least squares (OLS) regression.
comb_OLS(x)An object of class 'foreccomb'. Contains training set (actual values + matrix of model forecasts) and optionally a test set.
Returns an object of class foreccomb_res with the following components:
Returns the best-fit forecast combination method.
Returns the individual input models that were used for the forecast combinations.
Returns the combination weights obtained by applying the combination method to the training set.
Returns the intercept of the linear regression.
Returns the fitted values of the combination method for the training set.
Returns range of summary measures of the forecast accuracy for the training set.
Returns forecasts produced by the combination method for the test set. Only returned if input included a forecast matrix for the test set.
Returns range of summary measures of the forecast accuracy for the test set. Only returned if input included a forecast matrix and a vector of actual values for the test set.
Returns the data forwarded to the method.
The function integrates the ordinary least squares (OLS) forecast combination implementation of the ForecastCombinations package into ForecastComb.
The OLS combination method (Granger and Ramanathan (1984)) uses ordinary least squares to estimate the weights, \(\mathbf{w}^{OLS} = (w_1, \ldots, w_N)'\), as well as an intercept, \(b\), for the combination of the forecasts.
Suppose that there are \(N\) not perfectly collinear predictors \(\mathbf{f}_t = (f_{1t}, \ldots, f_{Nt})'\), then the forecast combination for one data point can be represented as: $$y_t = b + \sum_{i=1}^{N} w_i f_{it}$$
An appealing feature of the method is its bias correction through the intercept -- even if one or more of the individual
predictors are biased, the resulting combined forecast is unbiased. A disadvantage of the method is that it places no
restriction on the combination weights (i.e., they do not add up to 1 and can be negative), which can make interpretation
hard. Another issue, documented in Nowotarski et al. (2014), is the method's unstable behavior
when predictors are highly correlated (which is the norm in forecast combination): Minor fluctuations in the sample
can cause major shifts of the coefficient vector (‘bouncing betas’) -- often causing poor out-of-sample performance.
This issue is addressed by the comb_LAD method that is more robust to outliers.
The results are stored in an object of class 'foreccomb_res', for which separate plot and summary functions are provided.
Granger, C., and Ramanathan, R. (1984). Improved Methods Of Combining Forecasts. Journal of Forecasting, 3(2), 197--204.
Nowotarski, J., Raviv, E., Tr\"uck, S., and Weron, R. (2014). An Empirical Comparison of Alternative Schemes for Combining Electricity Spot Price Forecasts. Energy Economics, 46, 395--412.
Forecast_comb,
foreccomb,
plot.foreccomb_res,
summary.foreccomb_res,
accuracy
obs <- rnorm(100)
preds <- matrix(rnorm(1000, 1), 100, 10)
train_o<-obs[1:80]
train_p<-preds[1:80,]
test_o<-obs[81:100]
test_p<-preds[81:100,]
data<-foreccomb(train_o, train_p, test_o, test_p)
comb_OLS(data)
#> $Method
#> [1] "Ordinary Least Squares Regression"
#>
#> $Models
#> [1] "Series 1" "Series 2" "Series 3" "Series 4" "Series 5" "Series 6"
#> [7] "Series 7" "Series 8" "Series 9" "Series 10"
#>
#> $Fitted
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> [1] 0.423016743 -0.416757272 -0.600040581 0.132978812 0.025569531
#> [6] 0.004797593 0.802174635 0.040591412 0.095483977 0.324425064
#> [11] -0.023755458 0.157934371 0.112706808 0.447781913 0.465482123
#> [16] -0.362224126 0.511887394 -0.500068024 0.163515719 -0.342245036
#> [21] -0.005852930 -0.131010504 0.187045090 -0.420591474 0.466017546
#> [26] -0.436174703 0.082662528 0.457733090 0.021105103 0.169766680
#> [31] 0.485305798 0.193787112 0.533853794 0.920864614 0.082924260
#> [36] 0.371336798 -0.006623156 0.440338047 0.701477887 0.673015421
#> [41] 0.071713369 -0.044020132 0.209853303 0.441483494 0.323725180
#> [46] 0.780987474 -0.458776892 0.583102290 0.096976905 0.520039230
#> [51] 0.051088195 0.283519271 0.126136078 0.177245910 -0.504537996
#> [56] -0.297064432 0.048079458 -0.205270530 0.346393073 -0.312306084
#> [61] -0.276558514 -0.173754408 0.506693217 0.461497566 0.177894593
#> [66] 0.135179393 0.602939811 0.109433203 -0.069951042 0.053260549
#> [71] 0.187901558 -0.411638139 -0.486188275 0.090811754 -0.373300769
#> [76] -0.743713361 0.373254657 0.077956797 0.441385505 0.076049701
#>
#> $Accuracy_Train
#> ME RMSE MAE MPE MAPE ACF1
#> Test set -6.193505e-19 1.032368 0.8077056 93.51845 125.5303 -0.01611748
#> Theil's U
#> Test set 1.313787
#>
#> $Input_Data
#> $Input_Data$Actual_Train
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> [1] 0.25470325 0.02032630 0.98295225 -0.26206685 1.89997294 -0.04077880
#> [7] 0.59520888 0.77101983 -1.50672815 0.74892238 -0.10432356 -1.78216972
#> [13] -0.74798906 0.98021539 1.91700073 -0.55756282 0.49886915 -0.32948466
#> [19] -0.82866713 -0.91148372 0.59719097 0.90584143 0.41280731 0.63113533
#> [25] -0.83624473 -0.66873438 -0.99005285 1.57330702 2.92895756 0.28399229
#> [31] 0.57238248 0.07264993 -1.11045934 1.11059118 0.03106643 0.78798543
#> [37] -0.79402067 0.97828407 0.88219992 1.65414292 0.02837874 0.69606939
#> [43] -0.41551709 0.33778923 0.30690661 -0.78956898 -2.80540895 0.49899051
#> [49] -0.24975629 0.07582576 -1.12256455 1.46915230 0.20452337 1.40435824
#> [55] -2.40381404 0.95672516 1.16187717 -0.49907571 1.09000502 -0.56591032
#> [61] 0.47304995 1.43705882 1.81374203 -0.46431843 2.04643671 -0.71481789
#> [67] -0.35036600 0.88404582 -0.19407351 0.23495565 -1.22640892 0.31514897
#> [73] -1.98437703 -1.10654986 -1.85507815 -0.88119444 -0.37689758 2.03235753
#> [79] 0.58145368 -1.41635434
#>
#> $Input_Data$Forecasts_Train
#> Time Series:
#> Start = 1
#> End = 80
#> Frequency = 1
#> Series 1 Series 2 Series 3 Series 4 Series 5 Series 6
#> 1 -0.20040841 1.07638031 0.65575927 1.24192854 1.90662813 1.673023662
#> 2 3.16077911 -1.25667872 0.43604386 1.52038940 0.91384938 1.916447196
#> 3 0.70243584 1.45311576 0.47971136 1.74387567 2.36129944 1.402549521
#> 4 4.03844850 -0.92509223 1.81374941 1.66728781 -0.46367374 1.125941848
#> 5 1.33736282 2.40729338 2.08686118 1.61491363 0.43022851 -0.523021637
#> 6 1.41907991 -0.02310846 2.74136622 1.34912758 -0.49473706 1.003756472
#> 7 0.58455652 2.06872195 3.06235485 2.75319521 1.48991773 0.806722655
#> 8 1.16463450 1.72154323 -0.39617956 -0.07218288 2.93566721 0.851886490
#> 9 1.93225958 1.71356757 0.73646722 0.51132702 1.72771712 1.691738586
#> 10 0.01063740 2.92015032 1.39788379 0.64202524 1.21449015 2.301332000
#> 11 1.53648812 -0.96475333 0.74610151 2.50297325 1.74945862 1.976187190
#> 12 0.37825740 1.71970673 0.78279110 -0.27826424 0.94059431 0.582913122
#> 13 1.11026234 0.36117478 2.31925308 1.83509601 1.17888005 2.035272351
#> 14 -1.09268593 2.33714699 0.59394193 1.34756340 1.65252141 2.083939175
#> 15 0.09324169 0.77319834 2.65075137 2.21479317 1.04273150 1.270444049
#> 16 2.37057631 1.61201598 0.55090267 0.71140615 0.96415058 1.905375734
#> 17 0.87893635 1.34774540 1.57666305 0.34972280 1.47304664 -0.299291561
#> 18 1.40270971 0.62744295 -0.80923237 0.91073420 1.06946323 0.847828716
#> 19 -0.05823961 1.98699098 1.69011132 -0.24533066 0.42952622 3.222775274
#> 20 0.92020091 1.94220124 0.25588439 1.64176953 0.65176044 -1.257263831
#> 21 0.06610437 1.61434886 1.55088513 0.81907278 2.04976052 1.754436168
#> 22 -0.32865112 1.29756668 1.14143460 0.37049063 0.58442920 2.796327679
#> 23 2.47757814 0.34237175 0.30382708 1.00851585 0.90074351 -0.048516694
#> 24 0.28897113 1.99224502 0.51349045 1.48139742 0.41774340 0.494995644
#> 25 0.18560241 0.49261274 1.82864464 0.92920815 0.89753290 2.554265395
#> 26 0.03418280 0.91420315 0.01650212 1.21847578 1.34208749 1.897336890
#> 27 -0.21787873 2.62260314 1.94205778 2.39086434 0.52080770 0.551418644
#> 28 2.37309340 -0.37499385 1.52221011 0.86249807 0.15717532 1.243597768
#> 29 1.01101390 0.57710428 1.30026967 1.67033759 1.09755625 1.948890659
#> 30 -0.13986959 1.97631608 0.66943349 0.61213589 -0.32351956 -0.141329874
#> 31 1.28247779 1.04037462 1.04464643 0.90320074 1.97560685 1.449414336
#> 32 1.02152220 2.14970304 1.29238554 -0.01883212 1.26381757 0.889280823
#> 33 0.35039793 2.78452007 1.47923563 -0.64976624 -0.07546769 0.196502735
#> 34 1.75722274 1.03818276 2.22747680 2.39843365 1.31164126 0.582055614
#> 35 0.24294217 1.08695350 -0.19987001 0.60089026 -0.78896117 1.944991334
#> 36 1.76306573 1.91343427 1.84510395 0.67084436 2.71629313 0.160519641
#> 37 2.11947183 1.26337015 0.95841216 2.36047750 -1.52948348 -0.006880154
#> 38 1.63640340 0.98650762 1.75089587 0.86727824 1.38501735 1.517714682
#> 39 0.60292161 1.74655933 2.82682721 2.03261527 2.04606264 0.368030174
#> 40 2.10043709 1.37161521 1.97631707 0.30651310 3.08965928 -0.375749515
#> 41 -0.01085447 1.14668916 0.80448097 4.20820646 0.98585061 1.699649955
#> 42 0.80128175 0.70718192 0.33130057 0.23843006 0.98068921 3.361845443
#> 43 0.23191856 1.69748433 -0.52871314 -0.03747679 1.69864922 0.958839953
#> 44 -0.82611254 2.32914698 0.76647693 0.66473731 0.87157481 0.813791958
#> 45 2.20645067 1.49866954 0.31729679 0.92039162 -0.28752445 2.677929930
#> 46 1.02802289 2.12108894 2.18187797 0.69216066 1.37473441 0.188748077
#> 47 0.80551112 1.01724390 0.41422558 1.42157073 -0.04256547 0.727888353
#> 48 0.42304984 1.20673961 1.41374713 0.78772861 1.62717072 -0.872784891
#> 49 2.17102058 0.74043814 -0.50732864 -1.29019919 1.66088315 0.679291749
#> 50 1.17308698 0.40112551 1.09572123 0.09217744 0.79885082 1.804291983
#> 51 -0.69354873 1.44844653 0.83791338 1.85824078 1.33385424 -0.408891334
#> 52 0.33404365 1.54888795 0.43867409 -0.24086120 1.03801445 0.368557144
#> 53 0.19932223 0.09617886 2.32541683 1.70675249 0.50835111 2.350585659
#> 54 1.42485516 2.12602991 0.89965631 0.34014556 -0.57454979 0.448675616
#> 55 1.65636967 1.11760535 -0.02469573 2.92434758 0.78276509 1.013827905
#> 56 3.47437621 2.82695317 0.59376237 2.47362398 0.57401983 1.160023909
#> 57 3.11155431 0.56566760 1.65687600 -0.51775279 0.17994934 -1.151658928
#> 58 3.31651573 2.19772833 1.59041972 4.08587706 0.90892565 0.058879201
#> 59 2.28736142 0.76885254 2.66915180 1.17576390 0.10137941 1.845208702
#> 60 0.59927015 0.19050993 -0.07249866 1.12683620 -0.79723604 1.595009767
#> 61 0.89716756 2.09657428 0.59962491 0.59013333 1.04429438 1.567477274
#> 62 0.35321538 0.71408813 1.27361272 1.15900198 -0.80790681 -0.213646753
#> 63 0.23515318 -0.20101677 0.31792772 0.56276775 1.01244865 1.806663669
#> 64 0.67298839 2.19423587 2.08109102 -0.17469471 -0.25471118 1.024264818
#> 65 0.39094449 0.86843174 0.41020541 0.79082112 0.84869885 -0.332879860
#> 66 0.27515216 2.25564278 2.03197598 0.14068833 2.47088459 1.325468037
#> 67 2.47771053 0.86929848 2.85044566 -0.12147295 0.38258063 1.421280819
#> 68 1.62070404 1.30987335 1.08710444 1.84339072 1.98060485 -0.407563004
#> 69 0.27840922 1.03586709 1.24614502 0.32573905 0.08861201 1.948821364
#> 70 -0.61120728 1.69467434 0.05804176 1.50768591 0.25101021 -0.463566033
#> 71 2.14986314 1.22646793 0.88784054 0.58961746 1.63925814 1.395715166
#> 72 1.46757558 0.30584496 -0.07707298 1.14377945 0.73090737 -0.821153934
#> 73 1.08475564 -0.02453042 0.64782248 1.04528357 -0.23573349 1.185239095
#> 74 2.02348947 0.17425113 1.29818530 2.08393049 1.55057651 0.684823976
#> 75 1.97722632 -1.52875078 0.47469785 2.64422786 0.67412376 -0.018402436
#> 76 0.83011896 1.17138094 -0.06608009 1.17794045 -0.12510115 -0.956710753
#> 77 -0.11027409 0.17800213 0.44035475 0.68899366 3.92333129 -0.129695665
#> 78 1.29181735 1.20069159 1.26642584 1.17558267 -0.76005025 3.295425091
#> 79 1.45796483 1.24467521 2.09281374 0.71133776 0.57099282 0.190401879
#> 80 2.57338699 1.23409845 1.73919948 1.58193790 1.10857605 2.875790513
#> Series 7 Series 8 Series 9 Series 10
#> 1 0.93909229 -0.227265768 0.66759268 0.630664300
#> 2 2.06920733 1.574877052 1.93140852 0.775153527
#> 3 1.84167993 3.213721854 2.69097482 1.732678208
#> 4 -0.36801773 -0.306928591 1.66000943 1.305378964
#> 5 1.23869147 2.113895956 -0.11351057 1.751483142
#> 6 0.12068879 1.888096680 0.33628377 1.732639545
#> 7 2.42173609 -0.212977252 0.36018453 0.950678030
#> 8 2.10731843 1.227533586 0.44247470 1.188603034
#> 9 1.41030596 1.088409999 0.57418437 1.501269234
#> 10 1.81572855 1.213589923 1.62806695 0.453658851
#> 11 -0.94355132 0.049078524 2.56578330 1.233267034
#> 12 -0.22809072 0.899679987 1.03840932 1.511624827
#> 13 -0.34583799 2.614589821 0.92993726 0.330629983
#> 14 -0.60205578 -1.047948041 0.20818964 2.112107334
#> 15 -0.02995960 -0.007261052 0.50744732 2.091560201
#> 16 0.11155509 1.780528225 2.37899466 2.049482570
#> 17 0.69587240 -0.457039523 0.82972665 2.123994544
#> 18 0.30376573 -0.025526881 2.51522613 2.881901855
#> 19 3.17642953 1.653421172 0.34171045 1.193213487
#> 20 0.89472798 1.668445528 1.18911614 1.495895064
#> 21 3.25311827 1.286525492 2.05378488 2.506937372
#> 22 2.33416518 2.006022667 1.97298734 0.868220840
#> 23 1.87455251 0.475241121 -0.69096644 0.063264875
#> 24 1.57641625 2.110059759 2.14025724 1.245632309
#> 25 -0.36253997 -0.435958422 -0.31928934 2.191245917
#> 26 1.81730302 1.766745044 1.12994433 1.789589548
#> 27 1.04773103 1.749084292 0.32833865 1.227581732
#> 28 1.11775135 -0.365680432 1.49105059 -0.151543240
#> 29 0.45228608 1.390483456 0.04356851 1.293464602
#> 30 2.98256873 0.720874223 0.07720277 -0.001017768
#> 31 0.68250938 1.115969847 1.09146209 -0.931880806
#> 32 0.59238105 1.764426236 2.37143918 0.489552834
#> 33 -0.17652386 1.074631207 1.00608123 -0.268982369
#> 34 -1.85402512 -2.134918142 0.56319533 1.671540650
#> 35 1.82696236 0.196966428 -0.15028058 -0.642310961
#> 36 0.17549667 0.787570813 0.87643807 2.765032658
#> 37 0.04968163 0.164211020 0.71303328 0.234114295
#> 38 0.97872035 0.068841478 1.31979654 1.293608292
#> 39 0.85288804 -0.047050166 2.15020496 1.365017037
#> 40 1.27010999 1.483050067 -0.48037030 0.688245962
#> 41 0.95031275 0.400622488 0.67599090 -0.117564755
#> 42 0.68532827 -0.001866286 0.80268115 2.485225725
#> 43 1.19793623 -0.929441057 1.01602809 1.761795357
#> 44 1.25488563 -0.443573313 1.37953402 0.730173269
#> 45 0.12515025 0.025362192 -0.60600842 -0.555042549
#> 46 1.29981563 -0.136335624 2.96292371 -0.052347052
#> 47 2.29845632 1.719064039 2.57542045 0.880216850
#> 48 0.40652195 -0.043892433 1.68511780 0.430001374
#> 49 1.22899547 0.526356925 0.15439518 1.158627389
#> 50 2.25458338 -0.402345616 -0.66077013 0.701134258
#> 51 1.50463334 0.626059709 -0.25290886 1.946440061
#> 52 -0.04216592 0.705311815 0.61706397 0.527888254
#> 53 0.61237873 1.621853960 0.19173068 1.216211228
#> 54 0.89447633 -0.189525480 1.83966232 1.113175815
#> 55 1.40239108 1.694442488 -1.17615361 1.772300657
#> 56 2.11739647 1.753216074 0.85583608 1.033251075
#> 57 2.16766925 1.584102452 -0.39070296 1.926166760
#> 58 -0.07888894 2.367574043 -0.43620132 1.157300140
#> 59 0.70824587 0.921536873 1.43721170 0.911417347
#> 60 1.13433232 0.652049060 -0.33156925 1.099007731
#> 61 1.08249196 2.924804513 1.37997755 0.645937992
#> 62 -0.30226505 0.521106240 2.13230392 2.083365590
#> 63 1.31353900 -1.547605470 0.42007855 0.573607337
#> 64 -0.81737005 1.127589340 0.81110334 0.577092226
#> 65 2.97444780 0.432045495 0.39484374 0.193570832
#> 66 -0.62492004 2.155488971 2.07969126 2.731423622
#> 67 2.67062651 0.341019879 1.38818601 1.028515177
#> 68 -0.02380499 1.655224669 -1.21507869 1.484744622
#> 69 2.39618220 0.913659366 -0.03297668 2.397812168
#> 70 -0.17787227 0.151317505 0.97946091 0.588589024
#> 71 1.78287227 1.038309538 0.96887155 0.706550691
#> 72 0.08363444 0.404870131 3.82155580 1.998704226
#> 73 0.52149542 2.560104096 0.22919390 1.107906554
#> 74 2.78736947 0.481226972 1.47105433 1.128998711
#> 75 1.57988311 1.931962578 0.19685872 0.012441715
#> 76 0.29121954 1.484394987 3.34216731 2.571329874
#> 77 1.66572463 0.101189160 0.53584167 1.771087477
#> 78 0.90454188 1.428079030 -0.23935252 -0.169940047
#> 79 1.06841426 0.768086417 0.27216190 0.893844070
#> 80 1.97946053 -0.055672971 1.40353152 2.990411782
#>
#> $Input_Data$Actual_Test
#> [1] -0.08006549 -0.50600894 -1.73843695 -0.12956419 -0.21812288 -0.84018144
#> [7] -1.92998183 -1.16210604 0.11541613 -1.14671070 -1.98502776 -0.77790688
#> [13] -1.43290009 0.60689363 0.16575501 -0.24697557 -0.64020048 -1.01666243
#> [19] 1.03403406 0.81187543
#>
#> $Input_Data$Forecasts_Test
#> Series 1 Series 2 Series 3 Series 4 Series 5 Series 6
#> [1,] 1.6419174 0.86641135 0.84011486 1.42755607 -0.84793978 -1.14626659
#> [2,] 0.3738306 1.38324655 0.83722121 0.07405402 0.73945278 0.86985138
#> [3,] 1.6975170 -0.73535782 0.71351293 -1.52900062 1.84267633 -0.06954723
#> [4,] 1.9029731 1.48305800 2.58623419 2.09956200 0.62296395 -0.22099168
#> [5,] 2.2371222 0.43776048 0.59404159 0.90200621 0.95983981 1.91949107
#> [6,] 1.2141490 -0.08805016 0.26511704 1.42195410 1.58745834 0.45051177
#> [7,] 0.9112415 0.36670932 1.99899989 1.93186706 2.17691265 -0.03943532
#> [8,] 0.9262257 0.33021140 0.56255175 0.93562064 1.05803519 1.29697732
#> [9,] 0.2489347 -0.06213867 1.45123586 0.41728284 1.17759753 0.22275943
#> [10,] -0.9185981 0.47782068 1.58980054 0.92587526 1.04897061 0.07479102
#> [11,] 0.2832975 -0.51402002 0.07803332 1.48714585 1.02480761 1.36961686
#> [12,] 2.5908589 0.49343527 0.85639395 -0.81585595 -0.13799184 0.18182975
#> [13,] 0.8275007 0.62301467 -0.81100685 0.78528888 0.09647047 2.51577061
#> [14,] -0.2198385 -0.09868756 1.35245222 0.86028553 0.11788233 1.94873869
#> [15,] 0.3529274 2.66782994 1.54129838 0.37933861 0.84740469 0.60482405
#> [16,] 0.3156254 1.64205310 2.15743318 0.20948486 2.41331270 0.36242793
#> [17,] 1.0559069 -1.06238884 1.42358473 1.47594913 1.30404029 0.46889077
#> [18,] 2.2299852 2.37871968 1.82419655 -0.43480684 1.17418267 1.65518870
#> [19,] 2.4847295 1.27578468 1.01189698 0.66324681 0.22121957 0.95564075
#> [20,] 1.3820021 0.72692419 0.94523614 0.21093271 1.56207470 0.76921074
#> Series 7 Series 8 Series 9 Series 10
#> [1,] -0.2149257 0.2362492 0.3892118 -0.9574365
#> [2,] 2.0873692 0.3912520 2.2441817 0.9503309
#> [3,] 2.2795875 0.6004358 2.9097386 0.1339440
#> [4,] 1.2453706 1.2850713 2.1449950 0.2285957
#> [5,] 1.5076815 0.1025500 0.8051697 2.5654147
#> [6,] 0.2467540 1.1266065 0.9630607 1.4660534
#> [7,] 1.8462417 0.5104419 0.9175371 1.8205856
#> [8,] -0.4128461 0.6316598 2.7374832 1.0457135
#> [9,] 1.4594689 1.7750816 1.1155832 0.4665911
#> [10,] 2.5085626 1.5041025 2.0944515 0.6621737
#> [11,] 2.1882638 0.9952038 2.3841197 1.9648886
#> [12,] 0.5213700 0.3377195 1.7874568 1.2050087
#> [13,] 2.1791099 -1.1751978 0.1266573 0.1565797
#> [14,] 2.4881396 3.1116781 0.9864837 1.0542724
#> [15,] 2.4958565 1.3209563 -0.1644405 1.5869617
#> [16,] 1.1016987 0.4371525 1.6231557 2.2040881
#> [17,] 1.5638212 0.3742984 -0.3527784 0.8295184
#> [18,] -1.8017657 0.5182069 1.8234413 3.0776098
#> [19,] 0.9360053 1.1010440 -1.4312860 1.1319318
#> [20,] 0.2823630 2.4247818 3.0113830 1.3535829
#>
#>
#> $Predict
#> function (object, newpreds)
#> {
#> coef <- c(object$Intercept, object$Weights)
#> pred <- as.vector(coef %*% t(cbind(1, newpreds)))
#> return(pred)
#> }
#> <bytecode: 0x56049bd94a80>
#> <environment: namespace:ForecastComb>
#>
#> $Intercept
#> [1] 0.2144699
#>
#> $Weights
#> [1] -0.009176041 0.053834737 0.240633259 -0.089338476 0.117324144
#> [6] -0.007919222 -0.023697345 -0.204043755 -0.056075431 -0.150969918
#>
#> $Forecasts_Test
#> [1] 0.30987115 0.16160836 0.32441864 0.33993392 -0.11182091 -0.19302953
#> [7] 0.31579408 -0.04108829 0.12739531 0.08717766 -0.50574611 0.11517738
#> [13] 0.12391165 -0.45020103 0.22744230 0.54160229 0.28869059 0.29460813
#> [19] 0.12551086 -0.24795699
#>
#> $Accuracy_Test
#> ME RMSE MAE MPE MAPE
#> Test set -0.6475088 1.118259 0.9500539 139.1254 143.8849
#>
#> attr(,"class")
#> [1] "foreccomb_res"