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

• Published in: Mathematics (Basel) Vol. 12; no. 18; p. 2812
• Main Authors: Mu, Shengdong, Liu, Boyu, Gu, Jijian, Lien, Chaolung, Nadia, Nedjah
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; bidirectional long short-term memory network (BiLSTM); Data structures; Forecasts and trends; graph attention network; Machine learning; Neural networks; Nodes; Noise prediction; Securities markets; spatiotemporal attention; Spatiotemporal data; Stock exchanges; stock index prediction; Stock price indexes; Time series; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12182812

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.