A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Wei Bao; Jun Yue; Yulei Rao

doi:10.1371/journal.pone.0180944

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Open Access

Peer-reviewed

Research Article

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Wei Bao,

Affiliation Business School, Central South University, Changsha, China
⨯
Jun Yue ,

* E-mail: jyue@pku.edu.cn

Affiliation Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, China

http://orcid.org/0000-0002-6465-5052

⨯
Yulei Rao

Affiliation Business School, Central South University, Changsha, China
⨯

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Wei Bao,
Jun Yue,
Yulei Rao

Published: July 14, 2017
https://doi.org/10.1371/journal.pone.0180944

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It's great to provide the whole dataset

Posted by tthomas on 04 Mar 2019 at 11:18 GMT

Very nice dataset with six stock indices(CSI 300, Nifty 50, Hang Seng index, Nikkei 225, S&P500 and DJIA index)!

Our team use the dataset and the proposed method as a baseline to develop new models(e.g., Model-Agnostic model, Asynchronous Advantage Actor-Critic based model, Proximal Policy Optimization based model, Adversarial model, etc.)

Feel free to contact me if you are interested.

Taylor Thomas
taylor.t.cmu@gmail.com

No competing interests declared.

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A deep learning framework for financial time series using stacked autoencoders and long-short term memory

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

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It's great to provide the whole dataset

Posted by tthomas on 04 Mar 2019 at 11:18 GMT