Summary
International Symposium on Nonlinear Theory and its Applications
2005
Session Number:3-4-3
Session:
Number:3-4-3-1
Short Term Chaotic Time Series Prediction using Symmetric LS-SVM Regression
Marcelo Espinoza, Johan A.K. Suykens, Bart De Moor,
pp.606-609
Publication Date:2005/10/18
Online ISSN:2188-5079
Summary:
In this article, we illustrate the effect of imposing symmetry as prior knowledge into the modelling stage, within the context of chaotic time series predictions. It is illustrated that using Least-Squares Support Vector Machines with symmetry constraints improves the simulation performance, for the cases of time series generated from the Lorenz attractor, and multi-scroll attractors. Not only accurate forecasts are obtained, but also the forecast horizon for which these predictions are obtained is expanded.