[1]HAO Jianlong,LIU Zhibin,ZHANG Chen,et al.Stock trend prediction method based on improved Transformer and hypergraph model[J].CAAI Transactions on Intelligent Systems,2024,19(5):1126-1135.[doi:10.11992/tis.202308017]
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Stock trend prediction method based on improved Transformer and hypergraph model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1126-1135 Column: 学术论文—机器学习 Public date: 2024-09-05
Title:
Stock trend prediction method based on improved Transformer and hypergraph model
Author(s):
HAO Jianlong;  LIU Zhibin;  ZHANG Chen;  SUN Qiwei;  CHANG Xingong
School of Information, Shanxi University of Finance and Economics, Taiyuan 030006, China
Keywords:
Transformer; trend forecasting; attention mechanism; dynamic hypergraph; collaborative relationship; stock trend prediction; time series forecasting; hybrid model
CLC:
TP301.6
DOI:
10.11992/tis.202308017
Abstract:
Stock forecasting is an interesting but extremely difficult task. Stock time series prediction methods that incorporate relationship information have progressed in recent years, but the following issues remain. First, graph neural network-based methods consider simple pairwise relationships between stocks but ignore higher-order collaborative relationships. Second, most existing methods utilize the static relationship among stocks with predefined graphs, and modeling the potential changes in dynamic relationships among stocks is challenging. To address the abovementioned issues, a Dynamic HyperGraph Convolutional neural Network (DHGCN) framework for the end-to-end stock trend prediction is proposed. The temporal information of stocks is captured by an improved Transformer model, and the collaborative relationship information is integrated into the time series modeling by the static and dynamic hypergraphs. Experiments on the real-world datasets of the Chinese A-share market and the US stock market show that the prediction performance of the proposed model is significantly superior to that of the contemporary advanced models.
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Last Update: 2024-09-05

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