I am analyzing the spectral components of a time series using the continuous wavelet transform following Torrence and Compo (1998). I would like to partition the signal variability or spectral power across different scales (e.g. 50% of the variability is in the scales X to Y).
As pointed out here the global wavelet spectrum is a biased estimator of the power spectrum, so is there a way to plot the power spectrum in a normalized way, where I can compare the power at different frequencies?
Would it be correct to just reconstruct each scale range I am interested in and calculate the variance over time?