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.

翻译：无监督表示学习方法旨在从未标注数据中学习判别性特征表示，无需对每个样本进行标注。由于时间序列数据具有复杂特性且缺乏其他数据模态那样的视觉线索，其标注瓶颈尤为突出，因此实现无监督表示学习对此类数据至关重要。近年来，无监督表示学习技术在多个领域取得了快速发展。然而，针对时间序列的无监督表示学习方法仍缺乏系统性分析。为填补这一空白，我们对现有快速演进的时间序列无监督表示学习方法进行了全面文献综述。此外，我们开发了统一标准化的库ULTS（即时间序列无监督学习），以促进各类模型的快速实现与统一评估。借助ULTS，我们在9个不同的真实世界数据集上对前沿方法（尤其快速演进的对比学习方法）进行了实证评估。最后，我们讨论了时间序列无监督表示学习的实践考量与开放研究挑战，以推动该领域的未来研究。