基于非线性时间序列分析经验模态分解和小波分解异同性的研究

On the difference between empirical mode decomposition and wavelet decomposition in the nonlinear time series

基于经验模态分解(EMD)的希尔伯特变换(HT),是对非线性时间序列基于EMD进行分解,然后通过HT获得频谱.利用理想时间序列和青藏高原古里雅冰芯18O时间序列,系统地分析比较了EMD和小波分解(WD)以及HT和小波变换在非线性时间序列处理中的优劣,并针对它们各自的缺点提出了可能改进的设想.研究结果表明,将基于EMD的方法和基于WD的方法有机结合起来应用,可以更有效地识别原时间序列的特征信息.

The method of empirical mode decomposition (EMD) based Hilbert transformation is that separate the nonlinear time series into intrinsic mode functions (IMFs) with different time scale by using EMD, then apply the Hilbert transformation to every IMF to get the Hilbert spectrum. By systematically comparing the advantages and disadvantages between EMD and wavelet decomposition (WD), between Hilbert transformation and wavelet transformation in transacting nonlinear time series using ideal time series and O-18 time series of Guliya ice core on the Tibetan Plateau, we present some improving measures to reduce the disadvantages. The result of investigation show that the combination of EMD-based and WD-based methods may be more effectively in recognizing the main information of the time series.