Vector Quantization: A Weighted Version for Time-Series Forecasting

Author

Amaury Lendasse, Missouri University of Science and TechnologyFollow
D. Francois
V. Wertz
M. Verleysen

Abstract

Nonlinear Lime-Series Prediction Offers Potential Performance Increases Compared to Linear Models. Nevertheless, the Enhanced Complexity and Computation Time Often Prohibits an Efficient Use of Nonlinear Tools. in This Paper, We Present a Simple Nonlinear Procedure for Time-Series Forecasting, based on the Use of Vector Quantization Techniques; the Values to Predict Are Considered as Missing Data, and the Vector Quantization Methods Are Shown to Be Compatible with Such Missing Data. This Method Offers an Alternative to More Complex Prediction Tools, While Maintaining Reasonable Complexity and Computation Time. © 2004 Elsevier B.v. All Rights Reserved.

Recommended Citation

A. Lendasse et al., "Vector Quantization: A Weighted Version for Time-Series Forecasting," Future Generation Computer Systems, vol. 21, no. 7, pp. 1056 - 1067, Elsevier, Jan 2005.

The definitive version is available at https://doi.org/10.1016/j.future.2004.03.006

Department(s)

Engineering Management and Systems Engineering

Comments

National Aeronautics and Space Administration, Grant NCC-2-1244

Keywords and Phrases

Missing data; Radial-Basis Function Networks; Time-series prediction; Vector quantization

International Standard Serial Number (ISSN)

0167-739X

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 2005