| Multi-Scale Gaussian Processes： a Novel Model for Chaotic Time Series Prediction |
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| 引用本文： | 周亚同,张太镒,孙建成.Multi-Scale Gaussian Processes： a Novel Model for Chaotic Time Series Prediction[J].中国物理快报,2007,24(1):42-45. |
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| 作者姓名： | 周亚同 张太镒 孙建成 |
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| 作者单位： | [1]Department of Information and Communication Engineering, Xi＇an Jiaotong University, Xi＇an 710049 [2]Department of Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013 |
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| 基金项目： | Supported by the National Natural Science Foundation of China under Grant No 60602034. |
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| 摘 要： | Based on the classical Gaussian process （GP） model, we propose a multi-scale Gaussian process （MGP） model to predict the existence of chaotic time series. The MGP employs a covariance function that is constructed by a scaling function with its different dilations and translations, ensuring that the optimal hyperparameter is easy to determine. Moreover, the scaling function with its different dilations and translations can form a set of complete bases, resulting in the fact that the MGP can acquire better prediction performance than the GP. The experiments can lead to the following conclusions： （i） The MGP gives a relatively better prediction performance in comparison with the classical GP model. （ii） The prediction performance of the MGP is competitive with support vector machine （SVM）. They give better performance as compared to the radial basis function networks.
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| 关 键 词： | 时间混乱 多尺度工程 物理学 模型 |
| 修稿时间： | 2006-07-10 |
Multi-Scale Gaussian Processes: a Novel Model for Chaotic Time Series Prediction |
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| ZHOU Ya-Tong,ZHANG Tai-Yi,SUN Jian-Cheng.Multi-Scale Gaussian Processes: a Novel Model for Chaotic Time Series Prediction[J].Chinese Physics Letters,2007,24(1):42-45. |
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| Authors: | ZHOU Ya-Tong ZHANG Tai-Yi SUN Jian-Cheng |
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| Institution: | [1]Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049; [2]Department of Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013 |
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| Abstract: | Based on the classical Gaussian process (GP) model, we propose a multi-scale Gaussian process (MGP) model to predict the existence of chaotic time series. The MGP employs a covariance function that is constructed by a scaling function with its different dilations and translations, ensuring that the optimal hyperparameter is easy to determine. Moreover, the scaling function with its different dilations and translations can form a set of complete bases, resulting in the fact that the MGP can acquire better prediction performance than the GP. The experiments can lead to the following conclusions: (i) The MGP gives a relatively better prediction performance in comparison with the classical GP model. (ii) The prediction performance of the MGP is competitive with support vector machine (SVM). They give better performance as compared to the radial basis function networks. |
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| Keywords: | 05 45 -a 05 45 Tp 07 05 Mh |
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