Testing Stationarity

Testing stationarity, the assumption that a system's underlying dynamics remain constant over time, is crucial across diverse fields like machine learning and reinforcement learning. Current research focuses on developing robust methods to detect and handle non-stationarity, particularly in complex settings such as those involving neural networks and irregularly structured data (e.g., graphs), often employing latent factor models or change point detection algorithms. These advancements are vital for improving the reliability and performance of algorithms in real-world applications where the stationarity assumption frequently fails, leading to more accurate models and better decision-making in dynamic environments.