联合EMD和FSVM的非平稳时间序列预测

计算机科学 ›› 2014, Vol. 41 ›› Issue (Z11): 57-60.

联合EMD和FSVM的非平稳时间序列预测

龚邦明,王文波,赵攀

武汉科技大学理学院武汉430065;武汉科技大学理学院武汉430065;武汉科技大学理学院武汉430065

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金(11201354)资助

EMD-FSVM Prediction for Nonstationary Time Series

GONG Bang-ming,WANG Wen-bo and ZHAO Pan

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

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。

关键词: 非平稳时间序列,经验模态分解,模糊支持向量机,组合预测

Abstract: 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.

Key words: Non-stationary time series,Empirical mode decomposition,Fuzzy support vector machine,Combination forecast

龚邦明,王文波,赵攀. 联合EMD和FSVM的非平稳时间序列预测[J]. 计算机科学, 2014, 41(Z11): 57-60. https://doi.org/

GONG Bang-ming,WANG Wen-bo and ZHAO Pan. EMD-FSVM Prediction for Nonstationary Time Series[J]. Computer Science, 2014, 41(Z11): 57-60. https://doi.org/

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