Learning decent representations from unlabeled time-series data with temporal
dynamics is a very challenging task. In this paper, we propose an unsupervised
Time-Series representation learning framework via Temporal and Contextual
Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
First, the raw time-series data are transformed into two different yet
correlated views by using weak and strong augmentations. Second, we propose a
novel temporal contrasting module to learn robust temporal representations by
designing a tough cross-view prediction task. Last, to further learn
discriminative representations, we propose a contextual contrasting module
built upon the contexts from the temporal contrasting module. It attempts to
maximize the similarity among different contexts of the same sample while
minimizing similarity among contexts of different samples. Experiments have
been carried out on three real-world time-series datasets. The results manifest
that training a linear classifier on top of the features learned by our
proposed TS-TCC performs comparably with the supervised training. Additionally,
our proposed TS-TCC shows high efficiency in few-labeled data and transfer
learning scenarios. The code is publicly available at
this https URL

本文提出了一种通过时间和语境对比（TS-TCC）的无监督时间序列表示学习框架，该框架可从未标记数据中学习时间序列表示，其中包括弱和强扩充转换的两个不同但相关的视图，并提出了一种新的时间对比模块和一个上下文对比模块来学习稳健的时间表示和判别性表示。