Comparer des méthodes

	CEEMDAN	Transformée en ondelettes empiriques	Décomposition Variationnelle par Modes (VMD)
Domaine	Séries temporelles	Séries temporelles	Traitement du signal
Famille	Process / pipeline	Process / pipeline	Machine learning
Année d'origine	2011	2013	2014
Auteur d'origine	María E. Torres	Jérémie Gilles	Konstantin Dragomiretskiy & Dominique Zosso
Type	Non-stationary signal decomposition	Non-stationary signal decomposition	Adaptive variational signal decomposition algorithm
Source fondatrice	Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4144–4147).	Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010.	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.
Alias	CEEMDAN, Ensemble EMD with noise	EWT, Empirical wavelets	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
Apparentées	3	3	2
Résumé	Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved variant of empirical mode decomposition (EMD) that addresses mode-mixing artifacts through ensemble averaging with adaptive noise. Introduced by Torres and colleagues (2011), CEEMDAN decomposes signals into intrinsic mode functions (IMFs) representing oscillations at different scales. The method adds controlled noise to multiple realizations and averages the results, producing more stable, physically meaningful components than standard EMD.	The empirical wavelet transform (EWT) is a data-driven wavelet decomposition method that automatically defines wavelet bases adapted to the frequency content of the signal. Introduced by Jérémie Gilles (2013), it overcomes a key limitation of classical wavelets—which use fixed, predefined bases—by constructing custom wavelets from the signal's own spectrum. This adaptive approach is particularly effective for analyzing non-stationary signals with complex, multi-component structures.	Variational Mode Decomposition (VMD) is a fully adaptive, non-recursive signal decomposition method introduced by Konstantin Dragomiretskiy and Dominique Zosso in 2014. It decomposes a real-valued input signal into a discrete number of sub-signals, called intrinsic mode functions (IMFs), each with a specific sparsity in the frequency domain. Unlike Empirical Mode Decomposition, VMD frames decomposition as a variational optimization problem solved via the Alternating Direction Method of Multipliers (ADMM), yielding robust and physically meaningful components.