TY - GEN
T1 - Dynamical modeling with kernels for nonlinear time series prediction
AU - Ralaivola, Liva
AU - d'Alché-Buc, Florence
PY - 2004/1/1
Y1 - 2004/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84898989885
M3 - Conference contribution
AN - SCOPUS:84898989885
SN - 0262201526
SN - 9780262201520
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PB - Neural information processing systems foundation
T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Y2 - 8 December 2003 through 13 December 2003
ER -