  Unsupervised representation learning approaches aim to learn discriminative
feature representations from unlabeled data, without the requirement of
annotating every sample. Enabling unsupervised representation learning is
extremely crucial for time series data, due to its unique annotation bottleneck
caused by its complex characteristics and lack of visual cues compared with
other data modalities. In recent years, unsupervised representation learning
techniques have advanced rapidly in various domains. However, there is a lack
of systematic analysis of unsupervised representation learning approaches for
time series. To fill the gap, we conduct a comprehensive literature review of
existing rapidly evolving unsupervised representation learning approaches for
time series. Moreover, we also develop a unified and standardized library,
named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast
implementations and unified evaluations on various models. With ULTS, we
empirically evaluate state-of-the-art approaches, especially the rapidly
evolving contrastive learning methods, on 9 diverse real-world datasets. We
further discuss practical considerations as well as open research challenges on
unsupervised representation learning for time series to facilitate future
research in this field.
