Event Sequence

Event sequence analysis focuses on understanding and predicting patterns within ordered sets of events, often characterized by irregular timing and diverse data types. Current research emphasizes developing robust models, including transformers, recurrent neural networks, and temporal point processes, to handle these complexities across diverse domains like healthcare and finance, often incorporating self-supervised and contrastive learning techniques to improve representation learning. Standardized benchmarking efforts are underway to improve reproducibility and facilitate comparisons between different models, ultimately aiming to enhance the accuracy and interpretability of predictions for various real-world applications.