Package 'THETASVM'

Title: Time Series Forecasting using THETA-SVM Hybrid Model
Description: Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.
Authors: Fasila K. P. [aut, ctb], Mrinmoy Ray [aut, cre], Rajeev Ranjan Kumar [aut, ctb], K. N. Singh [aut, ctb], Amrender Kumar [aut, ctb], Santosha Rathod [aut, ctb]
Maintainer: Mrinmoy Ray <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2026-05-26 07:45:47 UTC
Source: https://github.com/cran/THETASVM

Help Index

Theta-SVM hybrid model fitting

Description

The THSVM function fit THETA-SVM hybrid model for time series data.

Usage

THSVM(data,h)

Arguments

data

Input univariate time series (ts) data.
h

The forecast horizon.

Details

This package allows you to fit the THETA-SVM hybrid model.

Value

Test_Result

Checking the suitability of data for hybrid modelling
THETA coefficients

Coefficients of the fitted THETA
SVM Summary

Summary of the fitted SVM model on residuals obtained from the fitted THETA model
Optimal Lag

Optimal Lag of the fitted SVM model
MAPE

Mean Absolute Percentage Error (MAPE) of the fitted hybrid model
MSE

Mean Square Error (MSE) of fitted hybrid model
fitted

Fitted values of hybrid model
forecasted.values

h step ahead forecasted values employing hybrid model

Author(s)

Fasila K. P., Mrinmoy Ray, Rajeev Ranjan Kumar, K. N. Singh, Amrender Kumar, Santosha Rathod

References

Bhattacharyya, A., Chakraborty, T., and Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, 107(3), 3025–3040.

See Also

ARSVM, ARIMAANN

Examples

data=lynx
THSVM(data,5)