Welcome to the InfoSci Platform
Reference Hub2
A Smart and Dynamic Decision Support System for Nonlinear Environments

A Smart and Dynamic Decision Support System for Nonlinear Environments

S. Uma, J. Suganthi
Copyright: © 2015 |Pages: 25
ISBN13: 9781466666399|ISBN10: 1466666390|EISBN13: 9781466666405
DOI: 10.4018/978-1-4666-6639-9.ch008
Cite Chapter Cite Chapter

MLA

Uma, S., and J. Suganthi. "A Smart and Dynamic Decision Support System for Nonlinear Environments." Recent Advances in Intelligent Technologies and Information Systems, edited by Vijayan Sugumaran, IGI Global, 2015, pp. 137-161. https://doi.org/10.4018/978-1-4666-6639-9.ch008

APA

Uma, S. & Suganthi, J. (2015). A Smart and Dynamic Decision Support System for Nonlinear Environments. In V. Sugumaran (Ed.), Recent Advances in Intelligent Technologies and Information Systems (pp. 137-161). IGI Global. https://doi.org/10.4018/978-1-4666-6639-9.ch008

Chicago

Uma, S., and J. Suganthi. "A Smart and Dynamic Decision Support System for Nonlinear Environments." In Recent Advances in Intelligent Technologies and Information Systems, edited by Vijayan Sugumaran, 137-161. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6639-9.ch008

Export Reference

Mendeley
Favorite

Abstract

The design of a dynamic and efficient decision-making system for real-world systems is an essential but challenging task since they are nonlinear, chaotic, and high dimensional in nature. Hence, a Support Vector Machine (SVM)-based model is proposed to predict the future event of nonlinear time series environments. This model is a non-parametric model that uses the inherent structure of the data for forecasting. The dimensionality of the data is reduced besides controlling noise as the first preprocessing step using the Hybrid Dimensionality Reduction (HDR) and Extended Hybrid Dimensionality Reduction (EHDR) nonlinear time series representation techniques. It is also used for subsequencing the nonlinear 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. A comparison of the experimental results of the proposed model with other models taken for the experimentation has proven that the prediction accuracy of the proposed model is outstanding.

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.