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mlpanda / DeepLearning_Financial

Attempt to replicate: A deep learning framework for financial time series using stacked autoencoders and long- short term memory
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Any progress #4

Open zhuygln opened 5 years ago

zhuygln commented 5 years ago

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

mlpanda commented 5 years ago

Hi Yonglin,

Not really as I am confident that the authors have made a mistake (probably used the WT on the entire time-series, hence peaking into the future). That said, I do think that parts of the paper makes sense for other applications, it's just not feasible to create those returns on daily S&P prices.

PS. have a look at this comment from a user on reddit: https://www.reddit.com/r/StockMarket/comments/cr9ncs/ive_reproduced_130_research_papers_about/

He mentions this paper:

"The most frustrating paper:

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)."

All the best, Marco