Analysis and applications of support vector forecasting model based on chaos theory

A novel support vector forecasting model based on chaos theory was presented. It adopted the support vector machines as a nonlinear forecaster and the network´s input variable number was determined through computing the reconstruct phase space´s saturated embedding dimension. The maximum effective forecasting steps was determined by computing the chaos time series´ largest Lyapunov exponent. It made use of support vector machines to carry out the nonlinear forecasting. Application results in an aeroengine compressor´s modeling show that the presented method possesses much better precision, which proves that the method is feasible and effective. This method is contributive and instructional for nonlinear time series forecasting via support vector machines for chaos time series.