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Recurrent Neural Networks for Financial Time-Series Modelling / Xianghua, Xie; Eddy, Deng; Jingjing, Deng

Pages: 892 - 897

Swansesa University Authors: Xianghua, Xie, Eddy, Deng, Eddy, Deng, Jingjing, Deng

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: College of Science
Start Page: 892
End Page: 897