| An improved method of support vector machine and its applications to financial time series forecasting |
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| 作者姓名： | LIANG Yanchun SUN Yanfeng |
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| 作者单位： | College of Computer Science and Technology, Jilin University, Changchun 130012, China,College of Mathematics, Jilin University, Changchun 130012, China |
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| 基金项目： | Supported by the Key Project of National Education Ministry of China (Grant No. 02090) |
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| 摘 要： | A novel method for kernel function of support vector machine is presented based on the information geometry theory. The kernel function is modified using a conformal mapping to make the kernel data-dependent so as to increase the ability of predicting high noise data of the method. Numerical simulations demonstrate the effectiveness of the method. Simulated results on the prediction of the stock price show that the improved approach possesses better forecasting precision and ability of generalization than the conventional models.
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| 关 键 词： | support vector machine (SVM) data-dependent information geometry conformal mapping financial tim |
An improved method of support vector machine and its applications to financial time series forecasting |
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| LIANG Yanchun,SUN Yanfeng.An improved method of support vector machine and its applications to financial time series forecasting[J].Progress in Natural Science,2003,13(9):696-700. |
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| Authors: | LIANG Yanchun SUN Yanfeng |
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| Institution: | 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
2. College of Mathematics, Jilin University, Changchun 130012, China |
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| Abstract: | A novel method for kernel function of support vector machine is presented based on the information geometry theory. The kernel function is modified using a conformal mapping to make the kernel data-dependent so as to increase the ability of predicting high noise data of the method. Numerical simulations demonstrate the effectiveness of the method. Simulated results on the prediction of the stock price show that the improved approach possesses better forecasting precision and ability of generalization than the conventional models. |
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| Keywords: | support vector machine (SVM) data-dependent information geometry conformal mapping financial tim |
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