Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model

Shengdong Mu, Boyu Liu (), Jijian Gu, Chaolung Lien and Nedjah Nadia
Additional contact information
Shengdong Mu: Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, China
Boyu Liu: School of Innovation and Entrepreneurship, Hubei University of Economics, Wuhan 430000, China
Jijian Gu: Chongqing Vocational College of Transportation, Chongqing 402200, China
Chaolung Lien: Department of Business Administration, International College, Krirk University, Bangkok 10220, Thailand
Nedjah Nadia: Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 205513, Brazil

Mathematics, 2024, vol. 12, issue 18, 1-20

Abstract: Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. The model enhances the analysis of temporal dependencies between data by introducing graph attention networks with multi-hop neighbor nodes while incorporating the temporal attention mechanism of long short-term memory (LSTM) to effectively address the potential interdependencies in the data structure. In addition, by assigning different learning weights to different neighbor nodes, the model can better integrate the correlation between node features. To verify the accuracy of the proposed model, this study utilized the closing prices of the Hong Kong Hang Seng Index (HSI) from 31 December 1986 to 31 December 2023 for analysis. By comparing it with nine other forecasting models, the experimental results show that the STBL model achieves more accurate predictions of the closing prices for short-term, medium-term, and long-term forecasts of the stock index.

Keywords: spatiotemporal attention; bidirectional long short-term memory network (BiLSTM); stock index prediction; graph attention network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/18/2812/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/18/2812/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:18:p:2812-:d:1475874

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().