  This paper presents TS2Vec, a universal framework for learning
representations of time series in an arbitrary semantic level. Unlike existing
methods, TS2Vec performs contrastive learning in a hierarchical way over
augmented context views, which enables a robust contextual representation for
each timestamp. Furthermore, to obtain the representation of an arbitrary
sub-sequence in the time series, we can apply a simple aggregation over the
representations of corresponding timestamps. We conduct extensive experiments
on time series classification tasks to evaluate the quality of time series
representations. As a result, TS2Vec achieves significant improvement over
existing SOTAs of unsupervised time series representation on 125 UCR datasets
and 29 UEA datasets. The learned timestamp-level representations also achieve
superior results in time series forecasting and anomaly detection tasks. A
linear regression trained on top of the learned representations outperforms
previous SOTAs of time series forecasting. Furthermore, we present a simple way
to apply the learned representations for unsupervised anomaly detection, which
establishes SOTA results in the literature. The source code is publicly
available at https://github.com/yuezhihan/ts2vec.
