Package {ECTSVR}

Title:	Error Correction Term Based Support Vector Regression for Time Series
Version:	0.2.0
Maintainer:	Pankaj Das <pankaj.iasri@gmail.com>
Description:	The cointegration based support vector regression model is a combination of error correction model and support vector regression. 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 mode. Implements the models with multiple kernel functions (linear, polynomial, radial, sigmoid) for time series forecasting. Includes stationarity tests, Johansen cointegration, and comprehensive model evaluation.
Depends:	R (≥ 3.5.0)
Imports:	urca, tseries, e1071, caret, Metrics, knitr, stats, graphics
Suggests:	rmarkdown, testthat
VignetteBuilder:	knitr
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2026-11-11 05:54:59 UTC; IASRI
Author:	Pankaj Das [aut, cre]
Repository:	CRAN
Date/Publication:	2026-11-12 16:40:15 UTC

Calculate Performance Metrics

Description

This function calculates common performance metrics for regression models including RMSE, MAE, MAPE, and R-squared.

Usage

calculate_metrics(actual, predicted)

Arguments

Value

A named numeric vector containing:

Examples

actual <- c(10, 12, 15, 18, 20)
predicted <- c(11, 13, 14, 17, 21)
metrics <- calculate_metrics(actual, predicted)
print(metrics)

Compare and Evaluate Models

Description

This function compares standard SVR and ECT-SVR models, identifies the best performing models based on different metrics, and creates a comparison table.

Usage

evaluate_models(std_results, ect_results, format = "simple", verbose = TRUE)

Arguments

Value

A list containing:

Examples

## Not run: 
# Assuming you have trained models
comparison <- evaluate_models(std_results, ect_results)
print(comparison$combined_results)
print(comparison$best_models$best_rmse)

# With markdown format
comparison <- evaluate_models(std_results, ect_results, format = "markdown")

## End(Not run)

Plot Model Comparisons

Description

This function creates visual comparisons between actual values, standard SVR predictions, and ECT-SVR predictions for different kernel types.

Usage

plot_comparisons(
  y_test,
  std_predictions,
  ect_predictions,
  kernels = c("linear", "polynomial", "radial", "sigmoid"),
  ylab = "Fruit Price"
)

Arguments

Value

No return value, creates plots in the current graphics device

Examples

## Not run: 
# After training models
plot_comparisons(
  data$y_test,
  std_results$predictions,
  ect_results$predictions
)

## End(Not run)

Prepare Data for ECT-SVR Analysis

Description

This function prepares time series data for ECT-SVR analysis by performing Johansen cointegration test, constructing error correction terms, creating lag features, and splitting data into training and test sets.

Usage

prepare_ectsvr_data(
  fruit,
  oil,
  train_ratio = 0.8,
  lags = 2,
  ect_sample = c("train", "full")
)

Arguments

Value

A list containing:

Examples

## Not run: 
# Generate example data
fruit <- rnorm(100, 100, 10)
oil <- rnorm(100, 50, 5)

# Prepare data
data <- prepare_ectsvr_data(fruit, oil)
str(data)

## End(Not run)

Main Function for ECT-SVR Analysis

Description

This function performs a complete ECT-SVR analysis including stationarity tests, cointegration tests, model training with multiple kernels, and comprehensive evaluation. It serves as the main entry point for the package.

Usage

run_ectsvr_analysis(
  fruit,
  oil,
  train_ratio = 0.8,
  kernels = c("linear", "polynomial", "radial", "sigmoid"),
  verbose = TRUE,
  plot_results = TRUE,
  lags = 2
)

Arguments

Value

A list containing all analysis results:

Examples

## Not run: 
# Load your data
fruit <- c(100, 102, 101, 105, 108, 107, 110, 112, 111, 115)
oil <- c(50, 51, 50.5, 52, 51.5, 53, 52.5, 54, 53.5, 55)

# Run complete analysis
results <- run_ectsvr_analysis(fruit, oil)

# View best model
print(results$best_model)

# View comparison table
print(results$evaluation$comparison_table)

## End(Not run)

Perform Johansen Cointegration Test

Description

This function performs the Johansen cointegration test on fruit and oil price series and extracts the cointegrating vector and error correction term.

Usage

test_cointegration(fruit, oil, verbose = TRUE)

Arguments

Value

A list containing:

Examples

## Not run: 
fruit <- rnorm(100, 100, 10)
oil <- rnorm(100, 50, 5)
coint <- test_cointegration(fruit, oil, verbose = TRUE)
print(coint$beta)
plot(coint$ECT, type = "l")

## End(Not run)

Perform Stationarity Tests on Time Series

Description

This function performs various stationarity tests including Jarque-Bera, Augmented Dickey-Fuller (ADF), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests on oil and fruit price series.

Usage

test_stationarity(oil, fruit, verbose = TRUE)

Arguments

Value

A list containing test results:

Examples

## Not run: 
fruit <- rnorm(100, 100, 10)
oil <- rnorm(100, 50, 5)
results <- test_stationarity(oil, fruit, verbose = TRUE)
print(results$adf_oil)

## End(Not run)

Train SVR Models with Different Kernels

Description

This function trains Support Vector Regression models using different kernel functions, with options for hyperparameter tuning. It scales the features, trains models for each specified kernel, and returns performance metrics.

Usage

train_svr_models(
  X_train,
  X_test,
  y_train,
  y_test,
  model_name,
  kernels = c("linear", "polynomial", "radial", "sigmoid"),
  cost = 100,
  gamma = 0.1,
  epsilon = 0.1,
  verbose = TRUE
)

Arguments

Value

A list containing:

Examples

## Not run: 
# Assuming prepared data from prepare_ectsvr_data()
data <- prepare_ectsvr_data(fruit, oil)
results <- train_svr_models(
  data$X_train_std,
  data$X_test_std,
  data$y_train,
  data$y_test,
  "Standard SVR"
)
print(results$results)

## End(Not run)