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灰色局部支持向量回归机及应用 被引量：2

Grey local support vector regression and its application
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摘要 为了解决全局支持向量回归机(Global-SVR)在大样本数据集中计算效率低下的问题,将局部支持向量回归机与灰色系统理论有机结合,并利用灰色关联度作为局部邻域函数构造灰色局部支持向量回归机(GL-SVR),该做法具有一定的理论价值.优化过程中采用留一法评估学习机的泛化性能,利用模式搜索法选择模型参数.真实的股价涨跌幅预测实验结果表明,该方法既加快了运算速度,又提高了预测精度. Due to much time consumption of global support vector regression （Global-SVR） for large sample size data,grey local SVR （GL-SVR） combined grey relational grade regarded as neighbourhood function with local support vector regression is considered. To optimize the machine,based on leave-one-out errors,pattern search method is adopted for model selection. Experiments are carried out on a real stock price change forecasting with GL-SVR and the results show that the proposed approach can not only speed up the computing speed,but also improve the prediction accuracy.
作者 蒋辉 王志忠
机构地区 中南大学数学科学与计算技术学院 惠州学院数学系
出处 《控制与决策》 EI CSCD 北大核心 2010年第3期399-403,共5页 Control and Decision
关键词 局部支持向量回归 灰色关联度 股价涨跌幅 Local support vector regression Grey relational grade Stock price change
分类号 O234 [理学—运筹学与控制论]
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参考文献14

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同被引文献27

  • 1梁庆卫,宋保维,吴朝晖.鱼雷使用维护费用灰色模型[J].系统仿真学报,2006,18(1):12-13. 被引量：4
  • 2王五祥,张维,崔和瑞,刘冰.多变量灰色模型MGM(1,n)在R&D投资预测中的应用[J].研究与发展管理,2006,18(2):92-96. 被引量：9
  • 3Jiang H, Wang Z. GMRVVm-SVR Model for Financial Time Series Forecasting [J]. Expert Systems with Applications, 2010, 37: 7813-7818.
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  • 6Huang Z, Chen H, Hsu C, Chen W, Wu S. Credit rating analysis with support vector machines and neural networks: A market comparative study [J]. Decision Support Systems, 2004, 37: 543- 558.
  • 7Kim K. Financial time series forecasting using support vector machines [J]. Neurocomputing, 2003(55): 307-319.
  • 8He W, Wang Z, Jiang H. Model optimizing and feature selecting for support vector regression in time series forecasting[J]. Neurocomputing, 2008, 72: 600-611.
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  • 10F.E.H. Tay, L.J. Cao, Modified support vector machines in financial time series forecasting [J], Neurocomputing, 2008, 48:847-861.

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控制与决策
2010年 第3期

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