Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
Abstract
In This Paper, We Investigate the Application of Adaptive Ensemble Models of Extreme Learning Machines (Elms) to the Problem of One-Step Ahead Prediction in (Non)stationary Time Series. We Verify that the Method Works on Stationary Time Series and Test the Adaptivity of the Ensemble Model on a Nonstationary Time Series. in the Experiments, We Show that the Adaptive Ensemble Model Achieves a Test Error Comparable to the Best Methods, While Keeping Adaptivity. Moreover, It Has Low Computational Cost. © 2009 Springer Berlin Heidelberg.
Recommended Citation
M. Van Heeswijk et al., "Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5769 LNCS, no. PART 2, pp. 305 - 314, Springer, Nov 2009.
The definitive version is available at https://doi.org/10.1007/978-3-642-04277-5_31
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Adaptivity; Ensemble models; Extreme learning machine; Nonstationarity; Sliding window; Time series prediction
International Standard Book Number (ISBN)
978-364204276-8
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
27 Nov 2009
Comments
Academy of Finland, Grant None