| Original Articles |
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Local Prediction of Chaotic Time Series Based on Support Vector Machine |
| LI Heng-Chao;ZHANG Jia-Shu |
| Sichuan Province Key Laboratory of Signal and Information Processing, Southwest Jiaotong University,
Chengdu 610031 |
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| Cite this article: |
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LI Heng-Chao, ZHANG Jia-Shu 2005 Chin. Phys. Lett. 22 2776-2779 |
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Abstract Based on phase space delay-coordinate reconstruction of a chaotic dynamics system, we propose a local prediction of chaotic time series using a support vector machine (SVM) to overcome the shortcomings of traditional local prediction methods. The simulation results show that the performance of this proposed predictor for making one-step and multi-step prediction is superior to that of the traditional local linear prediction method and global SVM method. In addition, it is significant that its prediction performance is insensitive to the selection of embedding dimension and the number of nearest neighbours, so the satisfying results can be achieved even if we do not know the optimal embedding dimension and how to select the number of nearest neighbours.
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| Keywords:
05.45.-a
05.45.Tp
07.05.Mh
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Published: 01 November 2005
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| PACS: |
05.45.-a
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(Nonlinear dynamics and chaos)
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05.45.Tp
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(Time series analysis)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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