方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 小波相干性× | 交叉小波变换× | |
|---|---|---|
| 领域 | 时间序列 | 时间序列 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1999 | 1998 |
| 提出者 | Christopher Torrence | Christopher Torrence |
| 类型≠ | Multi-scale correlation and phase | Bivariate wavelet interaction |
| 开创性文献≠ | Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690. DOI ↗ | Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. DOI ↗ |
| 别名≠ | WTC, Wavelet coherency, Continuous wavelet coherence | XWT, Cross-spectrum wavelet |
| 相关 | 1 | 1 |
| 摘要≠ | 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. | The cross-wavelet transform (XWT) is a bivariate extension of the continuous wavelet transform that measures the joint time-frequency representation of two signals. Introduced by Torrence and Compo (1998) and applied extensively by Grinsted, Moore, and Jevrejeva (2004) to geophysical data, XWT reveals where two signals share common spectral power and the phase relationship between them at each time-frequency point. This is the natural generalization of classical cross-spectral analysis to the time-varying domain. |
| ScholarGate数据集 ↗ |
|
|