# ECTSVR

## Introduction

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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