The field of financial market prediction is moving towards more complex and dynamic models that can capture temporal dependencies and inter-stock relationships. Researchers are exploring the use of graph neural networks, attention mechanisms, and Bayesian optimization to improve predictive performance. Notable papers in this area include MaGNet, which introduces a novel Mamba dual-hypergraph network for stock prediction, and DeltaLag, which proposes an end-to-end deep learning method for discovering dynamic lead-lag structures in financial markets. Another noteworthy paper is IVGAE-TAMA-BO, which presents a novel temporal dynamic variational graph model for link prediction in global food trade networks. These innovative approaches are advancing the field by providing more accurate and robust predictions, and are being applied to various domains such as stock movement prediction and food security monitoring.