Chaotic time series prediction using least squares
support vector machines
Ye Mei-Ying;Wang Xiao-Dong
【期刊名称】《中国物理：英文版》 【年(卷),期】2004(013)004
【摘要】We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction. 【总页数】5页(454-458)
【关键词】chaotic time series;time series prediction;support vector machines
【作者】Ye Mei-Ying;Wang Xiao-Dong
【作者单位】College of Mathematics and Physics, Zhejiang Normal University, Jinhua 321004, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua 321004, China
Chaotic time series prediction using least squares support vector machines
