Online Prediction

Online prediction focuses on developing algorithms that accurately forecast future events or values based on sequentially arriving data, aiming to minimize prediction error and computational cost. Current research emphasizes improving the efficiency and robustness of online prediction methods across diverse applications, exploring techniques like Gaussian processes, deep learning, and adaptive algorithms such as Follow-the-Perturbed-Leader, often within frameworks addressing privacy concerns or handling non-stationary data streams. These advancements have significant implications for various fields, including weather forecasting, network traffic management, and industrial process monitoring, enabling more efficient resource allocation and proactive risk mitigation.