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.
CITATION STYLE
Liitiäinen, E., & Lendasse, A. (2006). Long-term prediction of time series using state-space models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4132 LNCS-II, pp. 181–190). Springer Verlag. https://doi.org/10.1007/11840930_19
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