Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
Keywords:Graph Attention Network, Graph Neural Networks, Stock Prediction, Time Series
AbstractThe 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.
How to Cite
Lai, T.-Y., Cheng, W. J., & Ding, J.-E. (2023). Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16244-16245. https://doi.org/10.1609/aaai.v37i13.26982
AAAI Student Abstract and Poster Program