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Communication Dans Un Congrès Année : 2005

Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter

Résumé

In this paper, we propose a new model, the kernel Kalman Filter, to perform various nonlinear time series processing. This model is based on the use of Mercer kernel functions in the framework of the Kalman filter or linear dynamical systems. Thanks to the kernel trick, all the equations involved in our model to perform filtering, smoothing and learning tasks, only require matrix algebra calculus whilst providing the ability to model complex time series. In particular, it is possible to learn dynamics from some nonlinear noisy time series implementing an exact expectation-maximization procedure.
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Dates et versions

hal-00341953 , version 1 (26-11-2008)

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Liva Ralaivola, Florence d'Alché-Buc. Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter. Proceedings of IEEE International Joint Conference on Neural Networks 2005 (IJCNN 2005), 2005, Canada. pp.1449--1454, ⟨10.1109/IJCNN.2005.1556088⟩. ⟨hal-00341953⟩
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