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Conference Paper/Proceeding/Abstract 818 views 337 downloads

Recurrent Neural Networks for Financial Time-Series Modelling

Gavin Tsang, Jingjing Deng, Xianghua Xie Orcid Logo

Pages: 892 - 897

Swansea University Authors: Jingjing Deng, Xianghua Xie Orcid Logo

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...

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ISSN: 1051-4651
Published: Beijing, China 25th International Conference on Pattern Recognition 2018
Online Access: Check full text

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.
Keywords: Deep Learning, Neural networks, time series data analysis, financial modelling.
College: Faculty of Science and Engineering
Start Page: 892
End Page: 897