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Attempt to replicate: A deep learning framework for financial time series using stacked autoencoders and long- short term memory
Hello, I am paying attention to what you are doing, but the data link you provided is https://figshare.com/s/acdfb4918c0695405e33 I can't open it. He doesn't seem to be valid. Can you update the download link?
In addition, I also saw that you provided a piece of data, but can you add a data description? Otherwise I don't know what the meaning of the data is.
Thank you!
I tried implementing this too. 99% certain their implementation has the Wavelet transform leaking info. If you just do LSTM on daily close_price your odds are slightly better than a coin toss, adding WT they jump to around 65%. Running WT by windows so you don't leak info probability drops below 50%. SAE isn't going to recover anything from that.
I kinda elaborated a bit here
would love to hear your thoughts
RuntimeError: input and target shapes do not match: input [60 x 1], target [60] at c:\users\administrator\downloads\new-builder\win-wheel\pytorch\aten\src\thnn\generic/MSECriterion.c:13
Hi Marco,
Have you got any update about this replicating this?
I find this work very interesting for the probability to apply the method to nucleosynthesis network calculation.
Look forward to hearing more from you.
Yonglin Zhu
Ph.D. Candidate
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