基于平均包络线匹配算法的EMD端点效应分析及在股价趋势分解中的应用
发布时间：2019-05-07 21:02
【摘要】：经验模式分解(EMD)能够有效获得非平稳非线性信号的时频特征,但传统的EMD分解算法存在严重的端点效应.在深入研究和分析EMD算法的基础上,提出了一种基于波形匹配的端点效应处理方案,通过计算波形匹配度,在平均包络线内部寻找与其端部变化趋势最为接近的子波,并用这段子波代替平均包络线的边缘部分,使处理后的平均包络线极大地接近真实包络线,并把这种端点效应处理方案的EMD分解算法应用到实际的股票市场价格趋势分解中.实验结果表明,与经典的EMD边界延拓算法相比,本文提出的算法能更有效地抑制EMD分解时的边界效应,分解得到的固有模式函数更能体现模拟信号真实的频率、幅值信息.应用实验表明:与现有方法相比,该方法更能提高预测精度.
[Abstract]:Empirical mode decomposition (EMD) can effectively obtain the time-frequency characteristics of non-stationary nonlinear signals, but the traditional EMD decomposition algorithm has a serious endpoint effect. On the basis of in-depth study and analysis of EMD algorithm, an endpoint effect processing scheme based on waveform matching is proposed. By calculating the degree of waveform matching, the wavelet which is closest to the change trend of its end is found in the average envelope. This wavelet is used to replace the edge part of the average envelope so that the processed average envelope is very close to the real envelope. The EMD decomposition algorithm of the endpoint effect processing scheme is applied to the actual price trend decomposition of the stock market. The experimental results show that compared with the classical EMD boundary extension algorithm, the proposed algorithm can suppress the boundary effect of EMD decomposition more effectively, and the intrinsic mode function obtained from the decomposition can reflect the real frequency and amplitude information of the analog signal more effectively. The experimental results show that compared with the existing methods, the proposed method can improve the prediction accuracy.
【作者单位】： 华南理工大学经济与贸易学院;纽约州立大学石溪分校商学院;复旦大学计算机科学技术学院;华南理工大学工商管理学院;
【基金】：教育部人文社会科学研究项目(13YJC790068) 国家自然科学基金(10826053,70871040) 华南理工大学中央高校基本科研业务费专项资金(2009ZM0081)
【分类号】：F830.91;F224
[Abstract]:Empirical mode decomposition (EMD) can effectively obtain the time-frequency characteristics of non-stationary nonlinear signals, but the traditional EMD decomposition algorithm has a serious endpoint effect. On the basis of in-depth study and analysis of EMD algorithm, an endpoint effect processing scheme based on waveform matching is proposed. By calculating the degree of waveform matching, the wavelet which is closest to the change trend of its end is found in the average envelope. This wavelet is used to replace the edge part of the average envelope so that the processed average envelope is very close to the real envelope. The EMD decomposition algorithm of the endpoint effect processing scheme is applied to the actual price trend decomposition of the stock market. The experimental results show that compared with the classical EMD boundary extension algorithm, the proposed algorithm can suppress the boundary effect of EMD decomposition more effectively, and the intrinsic mode function obtained from the decomposition can reflect the real frequency and amplitude information of the analog signal more effectively. The experimental results show that compared with the existing methods, the proposed method can improve the prediction accuracy.
【作者单位】： 华南理工大学经济与贸易学院;纽约州立大学石溪分校商学院;复旦大学计算机科学技术学院;华南理工大学工商管理学院;
【基金】：教育部人文社会科学研究项目(13YJC790068) 国家自然科学基金(10826053,70871040) 华南理工大学中央高校基本科研业务费专项资金(2009ZM0081)
【分类号】：F830.91;F224
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