Recent studies have shown great promise in unsupervised representation
learning (URL) for multivariate time series, because URL has the capability in
learning generalizable representation for many downstream tasks without using
inaccessible labels. However, existing approaches usually adopt the models
originally designed for other domains (e.g., computer vision) to encode the
time series data and rely on strong assumptions to design learning objectives,
which limits their ability to perform well. To deal with these problems, we
propose a novel URL framework for multivariate time series by learning
time-series-specific shapelet-based representation through a popular
contrasting learning paradigm. To the best of our knowledge, this is the first
work that explores the shapelet-based embedding in the unsupervised
general-purpose representation learning. A unified shapelet-based encoder and a
novel learning objective with multi-grained contrasting and multi-scale
alignment are particularly designed to achieve our goal, and a data
augmentation library is employed to improve the generalization. We conduct
extensive experiments using tens of real-world datasets to assess the
representation quality on many downstream tasks, including classification,
clustering, and anomaly detection. The results demonstrate the superiority of
our method against not only URL competitors, but also techniques specially
designed for downstream tasks. Our code has been made publicly available at
this https URL

本文提出了一种利用基于形状的表示学习在无监督的多变量时间序列领域中的框架，其中采用了流行的对比学习方式来学习时间序列特定的形状特征，并设计了新的多粒度对比和多尺度对齐的学习目标来实现我们的目标，同时采用数据增强库来提高泛化性能。实验结果表明，我们的方法不仅优于 URL 竞争对手，而且优于专门为下游任务设计的技术。