东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2007, Vol. 28 ›› Issue (3): 325-328.DOI: -

• 论著 • 上一篇    下一篇

一种非线性非平稳时间序列预测建模方法

林树宽;杨玫;乔建忠;王国仁;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 出版日期:2007-03-15 发布日期:2013-06-24
  • 通讯作者: Lin, S.-K.
  • 作者简介:-
  • 基金资助:
    辽宁省自然科学基金资助项目(20042015)

Prediction modelling method for non-linear and non-stationary time series

Lin, Shu-Kuan (1); Yang, Mei (1); Qiao, Jian-Zhong (1); Wang, Guo-Ren (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2007-03-15 Published:2013-06-24
  • Contact: Lin, S.-K.
  • About author:-
  • Supported by:
    -

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摘要： 提出了一种基于经验模式分解和支持向量回归的非线性、非平稳时间序列预测建模方法.首先,针对时间序列的非平稳特征,通过经验模式分解将其分解为若干个本征模式分量,使其中每个分量均成为平稳序列;其次,对每个本征模式分量,基于支持向量回归建立相应的平稳时间序列预测模型;最后,再一次利用支持向量回归对这些预测模型进行非线性组合,得到非线性、非平稳时间序列的预测模型.仿真实验和工程应用均表明,所提的预测建模方法与传统的基于支持向量回归的建模方法相比,具有较高的精度,说明该方法对于非线性、非平稳时间序列的预测是有效的.

关键词: 经验模式分解, 支持向量回归, 非线性非平稳时间序列, 本征模式分量, 预测建模

Abstract: A prediction modelling method was proposed for non-linear and non-stationary time series, based on empirical mode decomposition (EMD) and support vector regression (SVR). The time series was decomposed into several intrinsic mode components (IMCs) via EMD so as to make every component stationary. Then in view of the stationary time series, a prediction model was developed correspondingly for each and every IMC on SVR basis, and these prediction models were non-linearly combined together by use of SVR again to form the final prediction model for non-linear and non-stationary time series. Both simulative experiment and engineering application showed that the proposed method has higher precision in comparison with the conventional SVR-based modelling method, i.e., effective to non-linear and non-stationary time series prediction.

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