| 基于稀疏型支持向量回归的时间序列预测 |
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| 引用本文： | 张军峰,隋东.基于稀疏型支持向量回归的时间序列预测[J].系统工程学报,2011,26(5). |
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| 作者姓名： | 张军峰 隋东 |
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| 作者单位： | 南京航空航天大学民航学院,江苏南京,210016 |
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| 基金项目： | 国家空中交通管制委员会基金资助项目 |
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| 摘 要： | 为提高支持向量回归在时间序列预测应用中的学习速度和泛化性能,提出了稀疏型支持向量回归方法.通过牛顿优化法,直接优化支持向量回归的原始问题.然后利用Cholesky分解更新原始优化中的Hessian矩阵实现稀疏型支持向量回归算法.最后将该算法运用到Mackey-Glass,Lorenz和Logistic混沌时间序列预测,仿真结果表明本文提出的方法能够在确保预测精度的前提下,有效地降低支持向量的个数.
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| 关 键 词： | 时间序列 支持向量同归 稀疏 牛顿优化法 |
Time series prediction based on sparse support vector regression |
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| ZHANG Jun-feng,SUI Dong.Time series prediction based on sparse support vector regression[J].Journal of Systems Engineering,2011,26(5). |
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| Authors: | ZHANG Jun-feng SUI Dong |
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| Institution: | ZHANG Jun-feng,SUI Dong (School of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China) |
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| Abstract: | Sparse support vector regression(SpSVR) method is proposed to improve the leaning speed and generalization performance in time series prediction.Firstly,the primal problem of support vector regression (SVR) is directly optimized through Newton optimization method.Then,in order to realize the sparseness of SVR,Cholesky decomposition is used to update the Hessian matrix in SVR primal problem.Finally,the proposed algorithm is apphed to Mackey-Glass,Lorenz and Logistic chaotic time series predictions.The simula... |
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| Keywords: | time series support vector machines sparse Newton optimization |
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