Conference Paper/Proceeding/Abstract 1139 views 455 downloads
Recurrent Neural Networks for Financial Time-Series Modelling
Gavin Tsang,
Jingjing Deng,
Xianghua Xie 
Pages: 892 - 897
Swansea University Authors:
Jingjing Deng, Xianghua Xie
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DOI (Published version): 10.1109/ICPR.2018.8545666
Abstract
In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to foreca...
| ISSN: | 1051-4651 |
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| Published: |
Beijing, China
25th International Conference on Pattern Recognition
2018
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa39477 |
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| Abstract: |
In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to forecast the next day closing price. |
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| Keywords: |
Deep Learning, Neural networks, time series data analysis, financial modelling. |
| College: |
Faculty of Science and Engineering |
| Start Page: |
892 |
| End Page: |
897 |