The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.
翻译:开发紧凑和节能的磨损传感器,增加了生物信号的可用性。分析这些不断记录且往往是多层面的规模时间序列,能够进行有意义的、不受监督的数据分割是一个吉祥的目标。实现这一目的的一个共同办法是在时间序列中找出作为分割基础的变化点。然而,传统的改变点检测算法往往有缺陷,限制了其真实世界的可应用性。值得注意的是,它们一般依赖可以提供的完整时间序列,因此无法用于实时应用。另一个常见的限制是,它们处理多维时间序列的分割不善(或无法),因此,这项工作的主要贡献是为名为“冷冻空间不受监督的断裂”的多维时间序列提出一种新的不受监督的分离算法(LS-US),该算法旨在方便地使用在线和批量数据。当将LS-US系统与其他状态的改变点检测算法进行比较时,对于各种现实世界的数据集来说,无论是在离线或系统运行S系统运行上,还是在实时设置上,都实现更好的性能和系统运行S。