Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| МОДВТ× | Вейвлетная когерентность× | |
|---|---|---|
| Область | Временные ряды | Временные ряды |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1995 | 1999 |
| Автор метода≠ | Donald B. Percival | Christopher Torrence |
| Тип≠ | Non-decimated multiresolution decomposition | Multi-scale correlation and phase |
| Основополагающий источник≠ | Percival, D. B., & Walden, A. T. (1995). Wavelet Methods for Time Series Analysis. Cambridge University Press. link ↗ | Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690. DOI ↗ |
| Другие названия | MODWT, Stationary wavelet transform, Undecimated DWT | WTC, Wavelet coherency, Continuous wavelet coherence |
| Связанные≠ | 2 | 1 |
| Сводка≠ | The maximal overlap discrete wavelet transform (MODWT) is a translation-invariant wavelet decomposition method that addresses a key limitation of the standard DWT: lack of shift invariance. Introduced by Percival and Walden (1995), MODWT applies the same wavelet filters at each scale without downsampling, producing an undecimated decomposition. Each detail and approximation coefficient array maintains the full length of the input signal, enabling both robust multi-scale analysis and translation-invariant 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. |
| ScholarGateНабор данных ↗ |
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