Spatiotemporal Modeling

Spatiotemporal modeling focuses on analyzing and predicting phenomena that evolve over both space and time, aiming to capture complex interactions and dependencies within datasets. Current research emphasizes improving the generalization ability of models, particularly using graph neural networks and transformers, to handle out-of-distribution scenarios and address computational limitations in large-scale applications like traffic forecasting and environmental monitoring. These advancements have significant implications for various fields, enabling more accurate predictions in areas such as urban planning, disease progression modeling, and climate change research.