Time Varying

Time-varying systems research focuses on modeling and analyzing systems whose properties or parameters change over time, aiming to accurately predict future behavior and understand underlying dynamics. Current research emphasizes developing robust models and algorithms, including neural networks (e.g., recurrent networks, transformers), Koopman operators, and Gaussian processes, to handle non-stationarity and time-dependent confounding in diverse applications. These advancements are crucial for improving forecasting accuracy in fields like healthcare, communication networks, and power grids, as well as enabling more effective control and decision-making in dynamic environments. The development of efficient and interpretable models for time-varying systems is a significant ongoing challenge.