Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines (1997)
| Venue: | IEEE Workshop on Neural Networks for Signal Processing VII |
| Citations: | 63 - 1 self |
BibTeX
@INPROCEEDINGS{Mukherjee97nonlinearprediction,
author = {Sayan Mukherjee and Edgar Osuna and Federico Girosi},
title = {Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines},
booktitle = {IEEE Workshop on Neural Networks for Signal Processing VII},
year = {1997},
pages = {511--519},
publisher = {IEEE Press}
}
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Abstract
A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on the same data base of chaotic time series that was used in [1] to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, Radial Basis Functions, and Neural Networks. The SVM performs better than the approaches presented in [1]. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM.
Citations
| 485 | Deterministic nonperiodic flow - Lorenz - 1963 |
| 145 | Support vector machines: Training and applications - Osuna, Freund, et al. - 1996 |
| 130 | Nonlinear prediction of chaotic time series - Casdagli - 1989 |
| 61 | Large-scale linearly constrained optimization - Murtagh, Saunders - 1978 |
| 18 | Multiple-valued stationary state and its instability of the transmitted light by a ring cavity system - Ikeda - 1979 |
| 11 | Deterministic nonperiodic ow - Lorenz - 1963 |
| 1 | Oscillation and chaos in physiological contol systems - Mackey, Glass - 1977 |
