विधियों की तुलना करें

	विच्छिन्न तरंगिका रूपांतरण	वेवलेट कोहेरेंस
क्षेत्र	समय श्रृंखला	समय श्रृंखला
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1992	1999
प्रवर्तक	Ingrid Daubechies	Christopher Torrence
प्रकार	Hierarchical signal decomposition	Multi-scale correlation and phase
मौलिक स्रोत	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.
उपनाम	DWT, Daubechies wavelets, Haar wavelet	WTC, Wavelet coherency, Continuous wavelet coherence
संबंधित	1	1
सारांश	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.