Nonlinear prediction of chaotic time series using support vectormachines
Mukherjee, S.
Osuna, E.
Girosi, F.
Center for Biol. & Comput. Learning, MIT, Cambridge, MA;
This paper appears in: Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Publication Date: 24-26 Sep 1997
On page(s): 511-520
Meeting Date: 09/24/1997 - 09/26/1997
Location: Amelia Island, FL, USA
ISBN: 0-7803-4256-9
References Cited: 10
INSPEC Accession Number: 5739878
Digital Object Identifier: 10.1109/NNSP.1997.622433
Posted online: 2002-08-06 20:57:22.0
Abstract
A novel method for regression has been recently proposed by Vapnik
et al. (1995, 1996). 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 a database of chaotic time series previously used 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 other approaches. We also study, for a
particular time series, the variability in performance with respect to
the few free parameters of SVM
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