Mutual Information and K-Nearest Neighbors Approximator for Time Series Prediction

Author

Antti Sorjamaa
Jin Hao
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

This Paper Presents a Method that Combines Mutual Information and K-Nearest Neighbors Approximator for Time Series Prediction. Mutual Information is Used for Input Selection. K-Nearest Neighbors Approximator is Used to Improve the Input Selection and to Provide a Simple But Accurate Prediction Method. Due to its Simplicity the Method is Repeated to Build a Large Number of Models that Are Used for Long-Term Prediction of Time Series. the Santa Fe a Time Series is Used as an Example. © Springer-Verlag Berlin Heidelberg 2005.

Recommended Citation

A. Sorjamaa et al., "Mutual Information and K-Nearest Neighbors Approximator for Time Series Prediction," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3697 LNCS, pp. 553 - 558, Springer, Dec 2005.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Input Selection; K-NN; Mutual Information; Time Series

International Standard Book Number (ISBN)

978-354028755-1

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

01 Dec 2005