Local prediction of non-linear time series using support vector regression
Prediction on complex time series has received much attention during the last decade. This paper reviews least square and radial basis function based predictors and proposes a support vector regression (SVR) based local predictor to improve phase space prediction of chaotic time series by combining the strength of SVR and the reconstruction properties of chaotic dynamics. The proposed method is applied to Hnon map and Lorenz flow with and without additive noise, and also to Sunspots time series. The method provides a relatively better long term prediction performance in comparison with the others.
local prediction support vector regression least square radial basis function time series analysis delay coordinates state space reconstruction