AAAI 2023technical3 citations
Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding
Abstract
The stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed “GAT-AGNN” module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets.
BibTeX
@article{Lai_Cheng_Ding_2024, title={Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26982}, DOI={10.1609/aaai.v37i13.26982}, abstractNote={The stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed “GAT-AGNN” module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lai, Tzu-Ya and Cheng, Wen Jung and Ding, Jun-En}, year={2024}, month={Jul.}, pages={16244-16245} }