Long-Term Prediction of Time Series Using State-Space Models,10.1007/11840930_19,Elia Liitiäinen,Amaury Lendasse

Long-Term Prediction of Time Series Using State-Space Models (Citations: 1)


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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 es- timation 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.

Conference: Int. Conference on Artificial Neural Networks - ICANN , pp. 181-190, 2006

DOI: 10.1007/11840930_19

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Long-Term Prediction of Time Series Using State-Space Models,10.1007/11840930_19,Elia Liitiäinen,Amaury Lendasse

References (15)

An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models (Citations: 90)

Learning nonlinear dynamical systems using an EM algorithm (Citations: 71)

Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models (Citations: 166)

Gaussian Filters for Nonlinear Filtering Problems (Citations: 225)

Kalman Filtering and Neural Networks (Citations: 243)

Citations (1)

Feature selection for time series prediction - A combined filter and wrapper approach for neural networks (Citations: 5)


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