  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.
