计算机应用研究2024，Vol.41Issue(7)：2126-2133,8.DOI:10.19734/j.issn.1001-3695.2023.11.0568
基于动态异构网络的股价预测
Stock price prediction based on dynamic heterogeneous network
摘要
Abstract
Stock prediction is typically a non-linear time series task.However,few researchers attempt to systematically re-veal the underlying structure of the stock market through technical data.The interactions of collaboration or conflicts among va-rious business domains can explain the fluctuations in stock.The incorporation of this additional information aids in predicting the future trends of stocks.In order to represent the trading situation of the stock market as realistically as possible and to ex-press the explicit or implicit relationships between stocks,this paper proposed a stock price prediction model sDHN based on a dynamic heterogeneous network,which synthesized the base of the stock and the industry and geographical information,and modeled it as a dynamic heterogeneous network.The model introduced dynamic time series capabilities to the network,and the algorithm creatively combined four different technical levels of similarity graphs of stock nodes to generate a rich information heterogeneous graph.Finally,it aggregated the semantic information hidden in different meta-paths to generate embeddings,exploring the potential correlations among stocks from the perspective of the heterogeneous graph.In addition,experiments on three real-world stock data sets show that the proposed model achieves accuracy improvements of between 5％and 34％over the overall baseline models.The F,-score is higher by approximately 11.5％～37％.It demonstrates through graphical analy-sis the effectiveness of this approach.关键词
股票预测/异构网络/图相似性Key words
stock prediction/heterogeneous network/graph similarity分类
信息技术与安全科学引用本文复制引用
韩忠明,孟怡新,郭惠莹,郭苗苗,毛雅俊..基于动态异构网络的股价预测[J].计算机应用研究,2024,41(7):2126-2133,8.基金项目
国家重点研发计划资助项目(2022YFC3302600) （2022YFC3302600）
北京市自然科学基金资助项目(4172016) （4172016）
