Sequential Prediction

Sequential prediction focuses on accurately forecasting future events or states based on observed sequences of data, aiming to improve prediction accuracy and robustness. Current research emphasizes developing advanced models, such as recurrent neural networks, graph neural networks, and state space models, often incorporating techniques like knowledge distillation and meta-learning to handle incomplete or high-dimensional data, and to improve long-horizon forecasting. These advancements are crucial for diverse applications, including healthcare diagnostics, financial forecasting, and human motion understanding, where reliable predictions are essential for decision-making. Furthermore, significant effort is dedicated to developing robust evaluation metrics and addressing challenges like calibration, handling adversarial examples, and mitigating the impact of data incompleteness.