Fast Bootstrap Applied to Ls-Svm for Long Term Prediction of Time Series

Author

Amaury Lendasse, Missouri University of Science and TechnologyFollow
Vincent Wertz
Geoffroy Simon
Michel Verleysen

Abstract

Time Series Forecasting is Usually Limited to One-Step Ahead Prediction. This Goal is Extended Here to Longer-Term Prediction, Obtained using the Least-Square Support Vector Machines Model. the Influence of the Model Parameters is Observed When the Time Horizon of the Prediction is Increased and for Various Prediction Methods. the Model Selection to Optimize the Design Parameters is Performed using the Fast Bootstrap Methodology Introduced in Previous Works.

Recommended Citation

A. Lendasse et al., "Fast Bootstrap Applied to Ls-Svm for Long Term Prediction of Time Series," IEEE International Conference on Neural Networks - Conference Proceedings, vol. 1, pp. 705 - 710, Institute of Electrical and Electronics Engineers, Dec 2004.

Department(s)

Engineering Management and Systems Engineering

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Dec 2004