Welcome to the InfoSci Platform
Reference Hub4
An Intelligent and Dynamic Decision Support System for Nonlinear Environments

An Intelligent and Dynamic Decision Support System for Nonlinear Environments

S. Uma, J. Suganthi
Copyright: © 2012 |Volume: 8 |Issue: 4 |Pages: 19
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781466613027|DOI: 10.4018/jiit.2012100104
Cite Article Cite Article

MLA

Uma, S., and J. Suganthi. "An Intelligent and Dynamic Decision Support System for Nonlinear Environments." IJIIT vol.8, no.4 2012: pp.43-61. http://doi.org/10.4018/jiit.2012100104

APA

Uma, S. & Suganthi, J. (2012). An Intelligent and Dynamic Decision Support System for Nonlinear Environments. International Journal of Intelligent Information Technologies (IJIIT), 8(4), 43-61. http://doi.org/10.4018/jiit.2012100104

Chicago

Uma, S., and J. Suganthi. "An Intelligent and Dynamic Decision Support System for Nonlinear Environments," International Journal of Intelligent Information Technologies (IJIIT) 8, no.4: 43-61. http://doi.org/10.4018/jiit.2012100104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Nonlinear time series systems are high dimensional and chaotic in nature. Since, the design of a dynamic and efficient decision making system is a challenging task, a Support Vector Machine (SVM) based model is proposed to predict the future event of a nonlinear time series environment. This model is a non-parametric model that uses the inherent structure of the data for forecasting. The Hybrid Dimensionality Reduction (HDR) and Extended Hybrid Dimensionality Reduction (EHDR) techniques are proposed to represent the time series data and to reduce the dimensionality and control noise besides subsequencing the time series data. The proposed SVM based model using EHDR is compared with the models using Symbolic Aggregate approXimation (SAX), HDR, SVM using Kernel Principal Component Analysis(KPCA) and SVM using varying tube size values for historical data on different financial instruments. The experimental results have proved that the prediction accuracy of the proposed model is better compared with other models taken for the experimentation.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.