Long-Term Prediction of Time Series Using State-Space Models
نویسندگان
چکیده
State-space models offer a powerful modelling tool for time series prediction. However, as most algorithms are not optimized for longterm 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.
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