中山大学学报自然科学版 ›› 2011, Vol. 50 ›› Issue (1): 53-57.

• 研究论文 • 上一篇    下一篇

基于缩减策略的混沌时间序列LSSVM预测模型

熊胜华,周翠英
  

  1. (中山大学工学院,广东 广州 510275)
  • 收稿日期:2009-12-19 修回日期:1900-01-01 出版日期:2011-01-25 发布日期:2011-01-25
  • 通讯作者: 周翠英

LSSVM Prediction Model for Chaotic Time Series Based on Reduction Strategy

XIONG Shenghua,ZHOU Cuiying   

  1. (School of Engineering,Sun Yatsen University,Guangzhou 510275,China)
  • Received:2009-12-19 Revised:1900-01-01 Online:2011-01-25 Published:2011-01-25

摘要: 由于混沌时间序列具有样本大等特点,使用最小二乘支持向量机(LSSVM)建立其预测模型具有内存开销大、训练速度慢等缺点,因此,在混沌序列数据特性的基础上,利用样本集分割与样本相关性的思想,提出一种基于缩减策略的混沌时间序列LSSVM预测模型。该模型利用混沌时间序列的平均周期将大样本数据分解成不同的子集,把最后一个子集之外的其他子集利用拉格朗日乘子的值缩减一部分非支持向量,将缩减后样本与最后一个子集合并,利用相关系数缩减法缩减合并后的样本集,并利用最小二乘支持向量机进行回归预测。最后通过相关实验,验证了本模型在基本不损失预测精度的基础上具有较快的计算速度。

关键词: 混沌时间序列, 最小二乘支持向量机, 缩减策略, 相关系数, 样本集分割

Abstract: As chaotic time series are large, least squares support vector machines(LSSVM) have disadvantages of bigger memory spending and slower training speed on prediction. According to data characteristic of large chaotic time series, it adopts ideas of data sets partition and data correlation coefficient to propose a LSSVM prediction model for large chaotic time series based on new reduction strategy. The model partitions large chaotic time series is split up into several different subsets based on the mean cycle of chaotic time series.Some nonsupport vectors from all subsets is reduced except the end based on the values of Lagrange multipliers.The reduced data sets combines with the end subset based on the correlation coefficients,and is used to regress and predict by LSSVM method. The proposed model is applied to the forecast of large chaotic time series on correlative experiments,and the results show it hardly loses prediction precision and takes quicker training speed.

Key words: chaotic time series, least squares support vector machines, reduction strategy, correlation coefficient, data sets partition

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