PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction
Liva Ralaivola and Florence d'Alché-Buc
In: NIPS 2003, 9-11 Dec 2003, Vancouver, Canada.


We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameters of the model, and second, to compute preimages of the time series predicted in the feature space by means of Support Vector Regression. Our model shows strong connection with the classic Kalman Filter model, with the kernel feature space as hidden state space. Kernel Dynamical Modeling is tested against two benchmark time series and achieves high quality predictions.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
Postscript - Requires a viewer, such as GhostView
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:484
Deposited By:Liva Ralaivola
Deposited On:24 December 2004