****
The cointegration based support vector regression model
is a combination of error correction model and support vector regression
(http://krishi.icar.gov.in/jspui/handle/123456789/72361).
This hybrid model allows the researcher to make use of the information
extracted by the cointegrating vector as an input in the support vector
regression model.
****
# Examples: How The cointegration based support vector regression model can be applied
library(ECTSVR)
#> Loading required package: urca
#> Loading required package: vars
#> Loading required package: MASS
#> Loading required package: strucchange
#> Loading required package: zoo
#>
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#>
#> as.Date, as.Date.numeric
#> Loading required package: sandwich
#> Loading required package: lmtest
#> Loading required package: WeightSVM
#taking data finland from the r library
data(finland)
#takaing the two cointegrated variables (4th and 3rd) from the data set
data_example <- finland[,4:3]
#application of ECTSVR model with radial basis kernel function of Epsilon support vector regression model
ECTSVR(data_example,"trace",0.8,2, "radial","eps-regression")
#> ect1
#> difp.l1 1.0000000
#> lnmr.l1 0.6242169
#> constant -0.1053779
#> 1 2 3 4 5 6
#> 0.007442106 0.007442106 0.007442106 0.013148864 0.013156226 0.013160313
#> 7 8 9 10 11 12
#> 0.007442106 0.013161295 0.007442106 0.007442106 0.007442106 0.007442106
#> 13 14 15 16 17 18
#> 0.013385461 0.007442106 0.012999794 0.007442106 0.013125496 0.013111320
#> 19 20 21 22 23 24
#> 0.013095157 0.013077146 0.013057422 0.026210381 0.012104142 0.012851940
#> 25 26 27 28 29 30
#> 0.007442106 0.012821802 0.012113769 0.007442106 0.012727457 0.012694920
#> 31 32 33 34 35 36
#> 0.012661944 0.012217532 0.012560905 0.012526685 0.012492270 0.012457693
#> 37 38 39 40 41 42
#> 0.014710853 0.025072055 0.012635627 0.007442106 0.012110174 0.007442106
#> 43 44 45 46 47 48
#> 0.012075790 0.007442106 0.007442106 0.012756543 0.007442106 0.011973554
#> 49 50 51 52 53 54
#> 0.011939825 0.012544914 0.012363622 0.012237262 0.011614601 0.011583429
#> 55 56 57 58 59 60
#> 0.016360967 0.013115034 0.013131312 0.014651619 0.021765443 0.030734678
#> 61 62 63 64 65 66
#> 0.012701692 0.031695378 0.022883156 0.028139471 0.012939760 0.027177371
#> 67 68 69 70 71 72
#> 0.022161979 0.031777042 0.013965855 0.032554597 0.013137541 0.026367146
#> 73 74 75 76 77 78
#> 0.012212128 0.028550475 0.021425224 0.012906297 0.012811597 0.013146492
#> 79 80 81 82 83 84
#> 0.012697656 0.013160313 0.011121547 0.013497269 0.012306289 0.012953645
#> 85 86 87 88 89 90 91
#> 0.01242638 0.02097188 0.03065588 0.01289139 0.01261942 0.01593844 0.01786930
#> 92 93 94 95 96 97 98
#> 0.01262654 0.01311432 0.01850902 0.01229004 0.01124902 0.01215068 0.01244322
#> 99 100 101 102 103 104
#> 0.02438293 0.01308238 0.01335810 0.00954113 0.01251213 0.01315131
#> [[1]]
#> RMSE_In_ECTSVR RMSE_out_ECTSVR MAD_In_ECTSVR MAD_out_ECTSVR MAPE_In_ECTSVR
#> [1,] 0.01304091 0.01417935 0.009891155 0.01153426 Inf
#> MAPE_out_ECTSVR
#> [1,] 0.5173228
#>
#> [[2]]
#> 85 86 87 88 89 90 91
#> 0.01242638 0.02097188 0.03065588 0.01289139 0.01261942 0.01593844 0.01786930
#> 92 93 94 95 96 97 98
#> 0.01262654 0.01311432 0.01850902 0.01229004 0.01124902 0.01215068 0.01244322
#> 99 100 101 102 103 104
#> 0.02438293 0.01308238 0.01335810 0.00954113 0.01251213 0.01315131