Comparison of Back Propagation, Long Short-Term Memory (LSTM), Attention-Based LSTM Neural Networks Application in Futures Market of China using R Programming
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1. | Title | Title of document | Comparison of Back Propagation, Long Short-Term Memory (LSTM), Attention-Based LSTM Neural Networks Application in Futures Market of China using R Programming |
2. | Creator | Author's name, affiliation, country | Wang Shuangao; Business School, China University of Political Science and Law, Xueyuan Road Campus: 25 Xitucheng Lu, Haidian District, Beijing, China; China |
2. | Creator | Author's name, affiliation, country | Liu Yi; Business School, China University of Political Science and Law, Xueyuan Road Campus: 25 Xitucheng Lu, Haidian District, Beijing, China |
2. | Creator | Author's name, affiliation, country | Rajchandar Padmanaban; NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal |
2. | Creator | Author's name, affiliation, country | Mohamed Shamsudeen; University V.O.C. College of Engineering, Anna University Thoothukudi Campus, 7th Street West, Bryant Nagar Main Road, Thoothukudi, Tamil Nadu, India |
2. | Creator | Author's name, affiliation, country | Subalakshmi R; University V.O.C. College of Engineering, Anna University Thoothukudi Campus, 7th Street West, Bryant Nagar Main Road, Thoothukudi, Tamil Nadu, India |
3. | Subject | Discipline(s) | |
3. | Subject | Keyword(s) | LSTM Neural Network; Futures Forecasting; Attention Mechanism; Financial Engineering |
4. | Description | Abstract |
Artificial neural network is widely used in the financial time series, but Long short-term memory (LSTM) neural network is rarely used in the futures market in China. In this paper, the LSTM neural network is studied by using futures data. The daily trading data of four groups of futures such as silver, copper, lithium and coking coal from December 2014 to December 2018 are used as the training object to make short-term prediction of the closing price. By comparing the Back Propagation (BP) neural network, general multi-layer LSTM neural network, and using the attention mechanism optimization LSTM contrast test, the result of the experiment shows that the futures price trend forecast time sequence, attention mechanism to promote significant effect of time sequence, and LSTM combined effect, by adjusting the parameters setting, using the improved LSTM neural network for time series prediction accuracy is higher, better generalization ability. |
5. | Publisher | Organizing agency, location | |
6. | Contributor | Sponsor(s) | |
7. | Date | (YYYY-MM-DD) | 2020-05-15 |
8. | Type | Status & genre | Peer-reviewed Article |
8. | Type | Type | |
9. | Format | File format | |
10. | Identifier | Uniform Resource Identifier | http://technical.cloud-journals.com/index.php/IJACSIT/article/view/998 |
11. | Source | Journal/conference title; vol., no. (year) | International Journal of Advanced Computer Science and Information Technology; 2020: Published Papers |
12. | Language | English=en | en |
14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
15. | Rights | Copyright and permissions |
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