联合EMD和FSVM的非平稳时间序列预测
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
提出一种基于经验模态分解(EMD)和模糊支持向量机(FSVM)的非平稳时间序列组合预测方法。首先,利用EMD对非平稳时间序列进行分解,将其分解为时间尺度特征较为单一的单模态分量,降低待预测信号的非线性复杂度;然后,利用模糊支持向量机对EMD分解后的各固有模态函数进行预测;最后将各固有模态函数独立预测的结果进行叠加,即可得到原始序列的预测值。以带噪声的Lorenz系统和太阳黑子月平滑值序列为实验数据,对提出的预测方法进行了仿真分析。实验结果表明,与BP神经网络预测和传统的SVM预测方法相比,提出的方法具有更好的预测精度,而且对带有孤立点、噪声的序列信号具有较强的适应能力。
This paper proposed a novel method to predict non-stationary time series,based on the empirical mode decomposition and fuzzy support vector machine.Firstly,uisng EMD,the non-stationary time series are decomposed into single modal components,reducing the prediction signal nonlinear complexity.Then,using the fuzzy support vector machine,each intrinsic mode function is predicted.Finally,the results predicted by each intrinsic mode function are superimposed to obtain the final forecast.Using Lorenz and sunspot month smooth value sequence with noise as the experimental data,our method was compared with BP neural network prediction and SVM prediction method by experiments.And this method has stronger adaptability to the sequence signal with isolated points and noise,and better prediction accuracy.
引文
[1]Vapnik V.The nature of statistical learning theory[M].Springer,2000
    [2]Chapelle,Olivier.Training a support vector machine in the primal[J].Neural Computation,2007,19(5):1155-1178
    [3]杨晓伟,郝志峰.支持向量机算法分析与设计[M].北京:科学出版社,2013
    [4]阳爱民.模糊分类模型及其集成方法[M].北京:科学出版社,2008
    [5]马芳芳,仝卫国,宋雨倩.模糊支持向量机的研究与应用[J].电脑与信息技术,2013(1):25-29
    [6]张永,迟忠先.基于时间序列的模糊支持向量回归[J].计算机工程,2007,33(19):47-48
    [7]Sun Z,Sun Y.Fuzzy support vector machine for regression estimation[C]∥IEEE International Conference on Systems,Man and Cybernetics,2003.IEEE,2003,4:3336-3341
    [8]Batuwita R,Plalde V.FSVM_CIL:fuzzy support vector machines for class imbalance learning[J].IEEE Transactions on Fuzzy Systems,2010,18(3):558-571
    [9]于德介,程军圣,杨宇.Hilbert-Huang变换在齿轮故障诊断中的应用[J].机械工程学报,2005,41(6):102-107
    [10]玄兆燕,杨公训.经验模态分解法在大气时间序列预测中的应用[J].自动化学报,2008,34(1):97-101
    [11]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995
    [12]吴炳胜,徐芮,姜金俊.基于EMD-SVM镜像延拓的转子故障诊断研究[J].河北工程大学学报:自然科学版,ISTIC,2012,29(1)
    [13]程军圣,于德介,杨宇.Hilbert-Huang变换端点效应问题的探讨[J].振动与冲击,2005,24(6):40-42
    [14]邓乃扬,田英杰.支持向量机——理论、算法与拓展[M].北京:科学出版社,2009
    [15]Stark J,Broomhead D S,Davies M E,et al.Takens embedding theorems for forced and stochastic systems[J].Nonlinear Analysis:Theory,Methods&Applications,1997,30(8):5303-5314
    [16]赵佩章,陈健,赵文桐.太阳黑子对厄尔尼诺,拉尼娜的影响[J].地球物理学进展,2001,16(3):85-90
    [17]方炜,刘春,张春生.太阳黑子与全球强震活动[J].高原地震,2003,15(4):27-31
    [18]庄得新,周玉芳.太阳黑子活动对近地空间的电磁辐射影响[J].北京理工大学学报,2005(1)

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心