Comparer des méthodes

	Décomposition Modale Empirique (DME)	La Transformée de Fourier et l'Analyse Spectrale (FFT)
Domaine	Traitement du signal	Traitement du signal
Famille	Machine learning	Machine learning
Année d'origine	1998	1965
Auteur d'origine	Norden Huang et al.	James Cooley & John Tukey (FFT)
Type	Adaptive data-driven decomposition algorithm	Frequency-domain decomposition algorithm
Source fondatrice	Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995.	Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301.
Alias	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma	Fast Fourier Transform, Discrete Fourier Transform, Spectral Analysis, Fourier Dönüşümü
Apparentées	3	2
Résumé	Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no predefined basis functions and derives all components directly from the signal itself, making it fundamentally different from Fourier or wavelet transforms.	The Fourier Transform decomposes a time-domain signal into its constituent sinusoidal frequencies, revealing the spectral content hidden within complex waveforms. Joseph Fourier introduced the continuous transform in 1822, but the computationally efficient Fast Fourier Transform (FFT) was formalized by James Cooley and John Tukey in 1965. Their landmark algorithm reduced the computational complexity from O(N²) to O(N log N), making large-scale spectral analysis practical across engineering, physics, and data science.