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So I've been researching wavelet transforms and FFT. I want to feed wavelet transforms of a 1D signal in time into a neural net and train against a target variable at each time step. The idea being the WT/FFT can help the net pick up on data structures.

Only issue being I have no idea how to feed the coefficients of the FFT into the model because my approximation signal is length 87 while the original series had 44,000 samples.

I looked into zero-padding to restore the number of samples but that is apparently a technique used on the time-series itself before decomposing with FFT.

Any help appreciated, hard to find material on exactly this subject.

paper im recreating: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180944

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I stumbled across this. http://ataspinar.com/2018/12/21/a-guide-for-using-the-wavelet-transform-in-machine-learning/

hope it helps john

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  • $\begingroup$ Thanks for the link, their visual of the model is actually quite helpful. will look into this. $\endgroup$ – Tyler Gaye Jun 13 at 9:38

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