Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2004 · 1.7K citations · 8 references
EngineeringElectrical EngineeringInformation TheorySpectroscopyTime Series AnalysisDigital Signal ProcessingHigh-frequency Data AnalysisNoiseSpectral AnalysisSpectral Data ProcessingTime-frequency AnalysisEmpirical Mode DecompositionEnergy DensityFrequency Spectrum AnalysisNoise Spectral AnalysisPower Spectral AnalysisPower Spectral DensityStatistical Significance TestingWhite Noise
The study proposes a method to assign statistical significance to IMF components in noisy data. The authors illustrate this method by applying it to Southern Oscillation Index data. Numerical experiments show that EMD behaves as a dyadic filter, producing normally distributed IMF components with identical Fourier spectra, and that the product of IMF energy density and averaged period is constant, with the energy‑density function following a chi‑squared distribution.
Based on numerical experiments on white noise using the empirical mode decomposition (EMD) method, we find empirically that the EMD is effectively a dyadic filter, the intrinsic mode function (IMF) components are all normally distributed, and the Fourier spectra of the IMF components are all identical and cover the same area on a semi–logarithmic period scale. Expanding from these empirical findings, we further deduce that the product of the energy density of IMF and its corresponding averaged period is a constant, and that the energy–density function is chi–squared distributed. Furthermore, we derive the energy–density spread function of the IMF components. Through these results, we establish a method of assigning statistical significance of information content for IMF components from any noisy data. Southern Oscillation Index data are used to illustrate the methodology developed here.
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Norden E. Huang, Steven Long, Man‐Li C. Wu et al. · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1998 · 23.3K citations · Full text
Computing, Computational Engineering, Biomedical Signal Processing +16
ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES
Michael Ghil, M. R. Allen, Michael D. Dettinger et al. · Reviews of Geophysics · 2002 · 2.2K citations · Full text