Graph Forecasting

Graph forecasting focuses on predicting the future state of interconnected systems represented as graphs, where nodes and edges evolve over time. Current research emphasizes developing sophisticated graph neural network architectures that effectively capture both the temporal dynamics of individual nodes and the spatial dependencies between them, often incorporating prior knowledge or learning graph structures from data. These advancements are crucial for improving predictions in diverse applications, such as sensor network monitoring, digital twin technology, and weather forecasting, where accurate and uncertainty-aware predictions are essential for informed decision-making. The field is also actively developing robust evaluation benchmarks and addressing challenges like handling missing data and adapting to non-stationary environments.