[1]郝剑龙,刘志斌,张宸,等.基于改进Transformer和超图模型的股票趋势预测方法研究[J].智能系统学报,2024,19(5):1126-1135.[doi:10.11992/tis.202308017]
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]
点击复制
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]
基于改进Transformer和超图模型的股票趋势预测方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1126-1135
栏目:
学术论文—机器学习
出版日期:
2024-09-05
- Title:
- Stock trend prediction method based on improved Transformer and hypergraph model
- Keywords:
- Transformer; trend forecasting; attention mechanism; dynamic hypergraph; collaborative relationship; stock trend prediction; time series forecasting; hybrid model
- 分类号:
- TP301.6
- 文献标志码:
- 2024-08-28
- 摘要:
- 股票预测是一项令人痴迷又极具挑战的任务。近年来，融合关系信息的股票时序预测方法取得一些进展，但仍存在如下问题：首先，基于图神经网络的方法仅考虑股票之间简单的成对关系，而未考虑股票间的高阶协同关系。其次，现有方法采用预定义图的方式直接给出股票间的静态关系，无法建模股票间潜在的动态变化关系。为了解决上述问题，提出一种端到端的动态超图卷积神经网络股票趋势预测框架。该框架基于改进的Transformer提取股票的时序信息，通过静态超图和动态超图将股票间的协同关系信息引入到时序建模中。在中国A股和美股市场数据集上的实验结果表明，与当前先进模型相比，本文模型的预测性能具有显著优势。
- 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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备注/Memo
收稿日期:2023-8-16。
基金项目:国家自然科学基金青年项目(62003198)；山西省基础研究计划自然科学研究面上项目(202203021221218).
作者简介:郝剑龙，讲师，博士，主要研究方向为深度学习、金融时序分析。主持国家自然科学基金青年项目1项，发表学术论文10余篇。E-mail：haojianlong2012@sxufe.edu.cn;刘志斌，硕士研究生，主要研究方向为金融时序分析、数据挖掘。E-mail：soberlau34@163.com;常新功，教授，博士，中国计算机学会高级会员，主要研究方向为图神经网络、数据挖掘、进化算法。发表学术论文30余篇。E-mail：c_x_g@126.com。
通讯作者:郝剑龙. E-mail：haojianlong2012@sxufe.edu.cn
更新日期/Last Update:
2024-09-05