Vertaile menetelmiä

	Hilbert-Huang-muunnos	Fourier-muunnos ja spektrianalyysi (FFT)
Tieteenala	Signaalinkäsittely	Signaalinkäsittely
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1998	1965
Kehittäjä	Norden Huang et al.	James Cooley & John Tukey (FFT)
Tyyppi	Adaptive time-frequency analysis method	Frequency-domain decomposition algorithm
Alkuperäislähde	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.
Rinnakkaisnimet	HHT, EMD-Hilbert Spectral Analysis, Hilbert Spektral Analizi, Adaptive Time-Frequency Decomposition	Fast Fourier Transform, Discrete Fourier Transform, Spectral Analysis, Fourier Dönüşümü
Liittyvät	2	2
Tiivistelmä	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.	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.