Abstract
Classical Nonlinear Models for Time Series Prediction Exhibit Improved Capabilities Compared to Linear Ones. Nonlinear Regression Has However Drawbacks, Such as overfilling and Local Minima Problems, User-Adjusted Parameters, Higher Computation Times, Etc. There is Thus a Need for Simple Nonlinear Models with a Restricted Number of Learning Parameters, High Performances and Reasonable Complexity. in This Paper, We Present a Method for Nonlinear Forecasting based on the Quantization of Vectors Concatenating Inputs (Regressors) and Outputs (Predictions). Weighting Techniques Are Applied to Give More Importance to Inputs and Outputs Respectively. the Method is Illustrated on Standard Time Series Prediction Benchmarks. © Springer-Verlag Berlin Heidelberg 2003.
Recommended Citation
A. Lendasse et al., "Nonlinear Time Series Prediction by Weighted Vector Quantization," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2657, pp. 417 - 426, Springer, Jan 2003.
The definitive version is available at https://doi.org/10.1007/3-540-44860-8_43
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-354044860-0
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2003
