Time series filtering, smoothing and learning using the kernel Kalman filter

Liva Ralaivola; Florence D'Alché-Buc

doi:10.1109/IJCNN.2005.1556088

Time series filtering, smoothing and learning using the kernel Kalman filter

Liva Ralaivola
, Florence D'Alché-Buc

Aix Marseille Université
Université d'Evry Val d'Essonne

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

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.

langue originale	Anglais
Pages	1454-1459
Nombre de pages	6
Les DOIs	https://doi.org/10.1109/IJCNN.2005.1556088
état	Publié - 1 janv. 2005
Modification externe	Oui
Evénement	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Durée: 31 juil. 2005 → 4 août 2005

Une conférence

Une conférence	International Joint Conference on Neural Networks, IJCNN 2005
Pays/Territoire	Canada
La ville	Montreal, QC
période	31/07/05 → 4/08/05

Accès au document

10.1109/IJCNN.2005.1556088

Contient cette citation

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title = "Time series filtering, smoothing and learning using the kernel Kalman filter",

abstract = "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.",

author = "Liva Ralaivola and Florence D'Alch{\'e}-Buc",

year = "2005",

month = jan,

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doi = "10.1109/IJCNN.2005.1556088",

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AU - D'Alché-Buc, Florence

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N2 - 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.

AB - 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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DO - 10.1109/IJCNN.2005.1556088

M3 - Paper

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T2 - International Joint Conference on Neural Networks, IJCNN 2005

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ER -

Time series filtering, smoothing and learning using the kernel Kalman filter

Résumé

Une conférence

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Empreinte digitale

Contient cette citation