| Chaotic time series prediction using least squares support vector machines |
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| 作者姓名： | 叶美盈 汪晓东 |
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| 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 |
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| 基金项目： | Project supported by the Zhejiang Provincial Natural Science Foundation, China (Grant No 602145). |
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| 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.
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| 关 键 词： | 混沌时间级数 时间级数预测 商业经济计划 天气预报 支持矢量 |
| 收稿时间： | 2003-07-10 |
| 修稿时间： | 8/1/2003 12:00:00 AM |
Chaotic time series prediction using least squares support vector machines |
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| Ye Mei-Ying and Wang Xiao-Dong.Chaotic time series prediction using least squares support vector machines[J].Chinese Physics B,2004,13(4):454-458. |
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| Authors: | Ye Mei-Ying and Wang Xiao-Dong |
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| Institution: | College of Information Science and Engineering, Zhejiang Normal University, Jinhua 321004, China; College of Mathematics and Physics, Zhejiang Normal University, Jinhua 321004, China |
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| Abstract: | 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. |
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| Keywords: | chaotic time series time series prediction support vector machines |
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