Exogenous Information

Exogenous information, encompassing external factors influencing a system's behavior, is a growing focus in various fields, aiming to improve prediction accuracy and decision-making by incorporating these external variables into models. Current research emphasizes integrating exogenous data into diverse architectures, including transformers, graph attention networks, and Koopman-based methods, to enhance forecasting in domains like time series analysis, traffic prediction, and reinforcement learning. This research is significant because effectively leveraging exogenous information leads to more robust and accurate models across numerous applications, from optimizing supply chains to improving autonomous systems.