Long-Term Prediction of Time Series using State-Space Models

Author

Elia Liitiäinen
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

State-Space Models Offer a Powerful Modelling Tool for Time Series Prediction. However, as Most Algorithms Are Not Optimized for Long-Term Prediction, It May Be Hard to Achieve Good Prediction Results. in This Paper, We Investigate Gaussian Linear Regression Filters for Parameter Estimation in State-Space Models and We Propose New Long-Term Prediction Strategies. Experiments using the Em-Algorithm for Training of Nonlinear State-Space Models Show that Significant Improvements Are Possible with No Additional Computational Cost. © Springer-Verlag Berlin Heidelberg 2006.

Recommended Citation

E. Liitiäinen and A. Lendasse, "Long-Term Prediction of Time Series using State-Space Models," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4132 LNCS thru II, pp. 181 - 190, Springer, Jan 2006.

The definitive version is available at https://doi.org/10.1007/11840930_19

Department(s)

Engineering Management and Systems Engineering

International Standard Book Number (ISBN)

978-354038871-5

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 Jan 2006