---
title: "ECTSVR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ECTSVR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction

\*\*\*\*<br/> *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.*

\*\*\*\*<br/>

```{r setup}
# Examples: How The cointegration based support vector regression model can be applied  
library(ECTSVR)
#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)
```
