Unsupervised Time Series Segmentation: A Survey on Recent Advances

Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD, we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category. As a conclusion, we found that: (1) Existing methods failed to provide sufficient support for online working, with only a few methods supporting online deployment; (2) Most existing methods require the specification of parameters, which hinders their ability to work adaptively; (3) Existing SD methods do not attach importance to accurate detection of boundary points in evaluation, which may lead to limitations in boundary point detection. We highlight the ability to working online and adaptively as important attributes of segmentation methods, the boundary detection accuracy as a neglected metrics for SD methods.