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.

本研究解决了高维时间序列数据分析中的挑战，尤其是在长期时间尺度上的潜在结构提取问题。通过回顾传统聚类方法与新兴深度学习算法的发展，提出了一种综合统一的分类法，为未来的时间序列聚类研究指明了方向。研究的主要发现是，结合传统与现代方法可以更好地揭示复杂数据集中的隐含模式。

弥合差距：时间序列聚类方法的十年回顾