Spatial-Temporal Graph Neural Networks Fusing Multiple Data | |
Xu,Haonan1,2; Xue, Wenfang1,2 | |
2023-03 | |
会议名称 | The 2022 4th International Conference on Frontiers Technology of Information and Computer |
会议日期 | 2022-12-2 |
会议地点 | Qingdao, China |
摘要 | Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. The application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. The experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods. |
七大方向——子方向分类 | 数据挖掘 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52016 |
专题 | 模式识别实验室 |
作者单位 | 1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xu,Haonan,Xue, Wenfang. Spatial-Temporal Graph Neural Networks Fusing Multiple Data[C],2023. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Spatial-Temporal_Gra(4434KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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