PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Long-Term Prediction of Time Series Using State-Space Models
Elia Liitiäinen and Amaury Lendasse
In: Artificial Neural Networks – ICANN 2006 Lecture Notes in Computer Science , 4132 . (2006) Springer Berlin , Berlin / Heidelberg , pp. 181-190. ISBN 978-3-540-38871-5


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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2578
Deposited By:Amaury Lendasse
Deposited On:22 November 2006