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This should be relatively straight-forward to do based on the existing WLS implementation. It is just a matter of shuffling the detail coefficients a bit differently.
The text was updated successfully, but these errors were encountered:
Implement the random cascade wavelet surrogate from Paluš, Milan. "Bootstrapping multifractals: Surrogate data from random cascades on wavelet dyadic trees." Physical review letters 101.13 (2008): 134101. This should be relatively straight-forward to do based on the existing WLS implementation. It is just a matter of shuffling the detail coefficients a bit differently.
I now have a working implementation of this method. It turned out that I had to use the regular discrete wavelet transform Wavelets.dwt (the number of coefficients at each levels increases as powers of 2) instead of the maximal overlap discrete wavelet transform (MODWT; the number of coefficients at each scale matches the length of the time series). As a result, I implemented it as a separate method RandomCascade, not as part of WLS.
Implement the random cascade wavelet surrogate from Paluš, Milan. "Bootstrapping multifractals: Surrogate data from random cascades on wavelet dyadic trees." Physical review letters 101.13 (2008): 134101..
This should be relatively straight-forward to do based on the existing WLS implementation. It is just a matter of shuffling the detail coefficients a bit differently.
The text was updated successfully, but these errors were encountered: