Event Sequence Data

Event sequence data, encompassing ordered occurrences of events over time, is analyzed to understand temporal dependencies and predict future events. Current research focuses on developing robust models, including transformers, Hawkes processes, and continuous-time convolutional networks, often employing self-supervised learning and score-matching techniques to improve representation learning and uncertainty quantification. These advancements are impacting diverse fields, from healthcare (analyzing electronic health records) to finance (modeling transaction sequences) and economics (predicting career trajectories), enabling more accurate predictions and insightful knowledge discovery. Furthermore, research addresses challenges like handling irregular sampling, missing data, and adversarial attacks to enhance the reliability and security of event sequence models.