Comparer des méthodes

	Transformée en ondelettes discrète	Cohérence par ondelettes
Domaine	Séries temporelles	Séries temporelles
Famille	Process / pipeline	Process / pipeline
Année d'origine	1992	1999
Auteur d'origine	Ingrid Daubechies	Christopher Torrence
Type	Hierarchical signal decomposition	Multi-scale correlation and phase
Source fondatrice	Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM.	Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690.
Alias	DWT, Daubechies wavelets, Haar wavelet	WTC, Wavelet coherency, Continuous wavelet coherence
Apparentées	1	1
Résumé	The discrete wavelet transform (DWT) is a fast, computationally efficient method for decomposing signals into different frequency and time components using orthogonal or biorthogonal wavelet functions. Developed rigorously by Ingrid Daubechies (1992) and built on Mallat's multiresolution decomposition theory (1989), the DWT employs filter banks to recursively split a signal into approximation (low-frequency) and detail (high-frequency) components. It has become the foundation for signal processing applications ranging from compression to feature extraction.	Wavelet coherence (WTC) is a normalized measure of correlation between two time series in the time-frequency domain, eliminating the amplitude-dependence of the raw cross-wavelet transform. Introduced by Torrence and Webster (1999) and formalized by Grinsted, Moore, and Jevrejeva (2004), WTC quantifies how tightly two signals are coupled at each time-frequency point, independent of their individual power levels. It is the wavelet analog of classical spectral coherence, revealing time-localized relationships across all frequencies.