****
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.
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# 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",verbose = FALSE)
#> [[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