---
title: "Introduction to ECTSVR"
author: "Pankaj Das"
date: "`r Sys.Date()`"
output: html_document
vignette: >
  %\VignetteIndexEntry{Introduction to 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 (<https://doi.org/10.1007/s40009-026-02355-9>). 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)
## Generate sample data
set.seed(123)
n <- 100
oil <- cumsum(rnorm(n, 0, 1)) + 50
fruit <- 2 * oil + rnorm(n, 0, 1) + 100

# Run analysis (quiet, no plots)
results <- run_ectsvr_analysis(fruit, oil, verbose = FALSE, plot_results = FALSE)

# View best model
print(results$best_model)
```