2024/12/29 by John Paparrizos, Fan Yang, Paparrizos, John +3 · 3 citations
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Bridging (networking) #Cluster analysis #Computer science #Computer security #Data mining #Data science #Geology #Machine learning #Series (stratigraphy) #Time Series Analysis and Forecasting #Time series
paper · pdf · doi:10.48550/arxiv.2412.20582
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.