Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis

Jing Yee Lim, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.

Original languageEnglish
Title of host publication3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665428996
DOIs
Publication statusPublished - 13 Sept 2021
Externally publishedYes
Event3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021 - Kota Kinabalu, Sabah, Malaysia
Duration: 13 Sept 202115 Sept 2021

Publication series

Name3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

Conference

Conference3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
Country/TerritoryMalaysia
CityKota Kinabalu, Sabah
Period13/09/2115/09/21

Keywords

  • Long Short-Term Memory
  • Stacked Bidirectional Long Short-Term Memory
  • Stock market prediction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis'. Together they form a unique fingerprint.

Cite this