| 基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题 |
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| 引用本文： | 郭辉,王玲,刘贺平.基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题[J].北京科技大学学报,2006,28(3):303-306. |
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| 作者姓名： | 郭辉 王玲 刘贺平 |
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| 作者单位： | 北京科技大学信息工程学院,北京,100083 |
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| 基金项目： | 高比容电子铝箔的研究开发与应用项目
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北京市教委重点学科建设项目 |
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| 摘 要： | 探讨了最小二乘支持向量机时间序列预测的方法,提出了用核主成分分析提取主元,然后用最小二乘支持向量机进行预测.通过实验表明,这种方法得到的效果优于没有特征提取的预测.同时与主成分分析提取特征相比,用核主成分分析效果更好.
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| 关 键 词： | 主成分分析 最小二乘支持向量机 核主成分分析 时间序列预测 核主成分分析 最小 支持向量机 结合 处理 时间序列 预测问题 problems time series forecasting support vector machines least squares principal component analysis kernel 分析效果 提取特征 特征提取 预测的方法 实验 主元 |
| 收稿时间： | 2005-01-24 |
| 修稿时间： | 2005-11-17 |
Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems |
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| GUO Hui,WANG Ling,LIU Heping.Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems[J].Journal of University of Science and Technology Beijing,2006,28(3):303-306. |
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| Authors: | GUO Hui WANG Ling LIU Heping |
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| Institution: | Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China |
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| Abstract: | This paper discusses least squares support vector machines(LSSVM)in the time series fore- casting problem.Kernel principal component analysis(KPCA)is proposed to calculate principal compo- nent.Least squares support vector machines are applied to predict time series.Experimental results show that the performance of LSSVM with feature extraction using KPCA is much better than that without fea- ture extraction.In comparison with PCA,there is also superior performance in KPCA. |
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| Keywords: | principal component analysis(PCA) least squares support vector machines(LSSVM) kernel principal component analysis(KPCA) time series forecasting |
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