Timothy Yu
Timothy Yu
will have to double check but i believe i was correct initially with `axis=0` - still may be worth running in parallel with `axis=1` with the extra column chopped off...
I am skeptical about the results from the model as described in the paper - that is why I am attempting to replicate both the model and apply to the...
@JannyKul by applying the wavelet transform separately on each `train-validate-test` split, no future information should be allowed to leak into the model: Example applied to `train-validate-test` split of first `csci300...
see https://github.com/timothyyu/wsae-lstm/issues/7 & https://github.com/timothyyu/wsae-lstm/commit/8073c426f903611f7ec22043d4b5378054b2904b the way I implemented the `train-validate-test` split + fit scaling with `RobustScaler` on the `train` set, transforming the `validate` and `test` sets respectively (per period) avoids/sidesteps...
issue reopened - this has not been entirely resolved yet, but I am confident I am on the right track 1. for each index dataset --- > split into 24...
> 3. for each train period, scale with fit_transform, and then apply the scaling from the train set on the validate and test sets, respectively Implemented as of v0.1.2 /...
``` def waveletSmooth( x, wavelet="haar", level=2, declevel=2): # calculate the wavelet coefficients coeff = pywt.wavedec( x, wavelet, mode='periodization',level=declevel,axis=0 ) # calculate a threshold sigma = mad(coeff[-level]) #print("sigma: ",sigma) uthresh =...
@JannyKul to prevent/side step the issue of the wavelet transform leaking data into the rest of the model, I'm going to see if I can save the `sigma` and `uthresh`...