IEEE Access Special Section: Sequential Data Modeling and Its Emerging Applications

Junchi Yan, Xiaoyong Pan, Liangda Li, Changsheng Li, Peng Cui, Chao Ma

Abstract: With the tremendous advance of technologies in data collection and storage, sequential data are becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as time series/video frames, biological data, and event data. It bears important practical utility for learning and understanding the dynamic behavior as well as the causality relationships across sequences, and it also calls for robust models to handle noisy and incomplete sequence data in real-world settings. Thus, machine-learning-based methods can be applied to efficiently analyze and model these sequential data.