ML Anthology

Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)

Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding
AAAI 2023 pp. 16244-16245
doi:10.1609/AAAI.V37I13.26982 /aaai/2023/lai2023aaai-sequential/

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.

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Cite

Text

Lai et al. "Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26982

Markdown

[Lai et al. "Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lai2023aaai-sequential/) doi:10.1609/AAAI.V37I13.26982

BibTeX

@inproceedings{lai2023aaai-sequential,
  title     = {{Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)}},
  author    = {Lai, Tzu-Ya and Cheng, Wen Jung and Ding, Jun-En},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16244-16245},
  doi       = {10.1609/AAAI.V37I13.26982},
  url       = {https://mlanthology.org/aaai/2023/lai2023aaai-sequential/}
}