Currently, the pycwt library includes a wavelet coherence (wct) function for measuring similarity between two signals. I propose the addition of a wavelet-based semblance function, as described by Cooper and Cowan (2008), to evaluate local phase relationships between time series as a function of both frequency and time. This method enhances the analysis of signal interactions by leveraging wavelet transforms to assess phase correlations, providing a more comprehensive understanding of temporal dynamics in multichannel data. Implementing this functionality will significantly improve the library's utility for researchers in fields such as geosciences and biomedical engineering etc.
Reference:
[1] G.R.J. Cooper, D.R. Cowan, "Comparing time series using wavelet-based semblance analysis," Computers & Geosciences, Volume 34, Issue 2, 2008, Pages 95-102, https://www.sciencedirect.com/science/article/pii/S0098300407001185.
Currently, the pycwt library includes a wavelet coherence (wct) function for measuring similarity between two signals. I propose the addition of a wavelet-based semblance function, as described by Cooper and Cowan (2008), to evaluate local phase relationships between time series as a function of both frequency and time. This method enhances the analysis of signal interactions by leveraging wavelet transforms to assess phase correlations, providing a more comprehensive understanding of temporal dynamics in multichannel data. Implementing this functionality will significantly improve the library's utility for researchers in fields such as geosciences and biomedical engineering etc.
Reference:
[1] G.R.J. Cooper, D.R. Cowan, "Comparing time series using wavelet-based semblance analysis," Computers & Geosciences, Volume 34, Issue 2, 2008, Pages 95-102, https://www.sciencedirect.com/science/article/pii/S0098300407001185.