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应用科学学报

• 信号与信息处理 • 上一篇    下一篇

混沌时间序列变分贝叶斯回归预测

汪金菊1,2，徐小红1，朱功勤1,2 ,黄国林2
  

  1. 1.合肥工业大学 计算机与信息学院,安徽 合肥 230009;

    2.合肥工业大学 数学系,安徽 合肥 230009

  • 收稿日期:2007-12-04 修回日期:2008-04-13 出版日期:2008-07-31 发布日期:2008-07-31

Chaotic Time Series Prediction Using Variational Bayesian Regressive Model

WANG Jin-ju1,2, XU Xiao-hong1, ZHU Gong-qin1,2 , HUANG Guo-lin2   

  1. 1. School of Computer and Information Science, Hefei University of Technology, Hefei 230009, China;
    2. Department of Mathematics, Hefei University of Technology, Hefei 230009, China
  • Received:2007-12-04 Revised:2008-04-13 Online:2008-07-31 Published:2008-07-31

摘要： 基于变分贝叶斯及相空间重构理论，提出了含噪混沌时间序列相空间域线性回归预测模型。该模型对序列进行相空间重构，在相空间中用变分贝叶斯推断方法估计线性回归系数。将该模型对含加性高斯噪声的Mackey-Glass混沌时间序列进行了预测研究。仿真结果表明,该文方法能够有效地抑制过拟和现象具有较强的抗噪声能力，且预测结果对重构相空间的嵌入维数和时间延迟的变化不敏感。

关键词: 混沌时间序列, 变分贝叶斯, 相空间, 线性回归模型, 预测

Abstract: We present a linearly regressive prediction model for noisy chaotic time series phase space based on variational Bayesian and phase space reconstructive theory. Time series phase space is constructed. The variational Bayesian method estimates the linear regressive coefficient. We use the model to predict the Mackey-Glass chaotic time series with additive Gaussian noise. The results show that the model is robust to noise and can effectively control over-fitting. The prediction effect is not sensitive to the change of embedding dimension and time delay.

Key words: chaotic time series, variational Bayesian, phase space, linear regressive model, prediction