I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think)."
- Not sure how author tried to implement it , but is this not how you train LSTM networks by feeding t+1 data back into the cell again to predict t+2 data. It will be easier if author made it open source as well
I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think)."
- Not sure how author tried to implement it , but is this not how you train LSTM networks by feeding t+1 data back into the cell again to predict t+2 data. It will be easier if author made it open source as well