Non Stationary

Non-stationarity, the presence of time-varying statistical properties in data, poses a significant challenge across numerous scientific fields. Current research focuses on developing models and algorithms that can effectively capture and account for these dynamic changes, employing techniques such as Gaussian processes with time-varying kernels, deep learning architectures (e.g., transformers, convolutional neural networks), and adaptive methods like online learning and continual learning. These advancements are crucial for improving the accuracy and reliability of predictions and decision-making in diverse applications, ranging from system identification and time series forecasting to reinforcement learning and recommendation systems. The ultimate goal is to move beyond the simplifying assumption of stationarity, enabling more realistic and robust modeling of complex real-world phenomena.