Long-Term prediction of time series using state-space models

Authors:
Elia Liitiäinen;Amaury Lendasse
Affiliations:
Neural Networks Research Centre, Helsinki University of Technology, Finland;Neural Networks Research Centre, Helsinki University of Technology, Finland
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

Citing 8
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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.