手法を比較

	離散ウェーブレット変換	ウェーブレットコヒーレンス
分野	時系列解析	時系列解析
系統	Process / pipeline	Process / pipeline
提唱年	1992	1999
提唱者	Ingrid Daubechies	Christopher Torrence
種類	Hierarchical signal decomposition	Multi-scale correlation and phase
原典	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.
別名	DWT, Daubechies wavelets, Haar wavelet	WTC, Wavelet coherency, Continuous wavelet coherence
関連	1	1
概要	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.