Comparar métodos

	Descomposición Empírica por Modos (EMD)	Transformada de Hilbert-Huang
Campo	Procesamiento de señales	Procesamiento de señales
Familia	Machine learning	Machine learning
Año de origen	1998	1998
Autor original	Norden Huang et al.	Norden Huang et al.
Tipo	Adaptive data-driven decomposition algorithm	Adaptive time-frequency analysis method
Fuente seminal	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.	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.
Alias	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma	HHT, EMD-Hilbert Spectral Analysis, Hilbert Spektral Analizi, Adaptive Time-Frequency Decomposition
Relacionados	3	2
Resumen	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 Hilbert-Huang Transform (HHT) is an adaptive, data-driven method for analyzing non-linear and non-stationary time series, introduced by Norden E. Huang and colleagues in 1998. It combines Empirical Mode Decomposition (EMD), which decomposes a signal into intrinsic mode functions (IMFs), with the Hilbert spectral analysis to produce instantaneous frequency and amplitude representations without assuming signal stationarity or linearity.