Short Term Chaotic Time Series Prediction using Symmetric LS-SVM Regression
Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor · 2005
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.