  Learning time-series representations when only unlabeled data or few labeled
samples are available can be a challenging task. Recently, contrastive
self-supervised learning has shown great improvement in extracting useful
representations from unlabeled data via contrasting different augmented views
of data. In this work, we propose a novel Time-Series representation learning
framework via Temporal and Contextual Contrasting (TS-TCC) that learns
representations from unlabeled data with contrastive learning. Specifically, we
propose time-series-specific weak and strong augmentations and use their views
to learn robust temporal relations in the proposed temporal contrasting module,
besides learning discriminative representations by our proposed contextual
contrasting module. Additionally, we conduct a systematic study of time-series
data augmentation selection, which is a key part of contrastive learning. We
also extend TS-TCC to the semi-supervised learning settings and propose a
Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data
to further improve representations learned by TS-TCC. Specifically, we leverage
the robust pseudo labels produced by TS-TCC to realize a class-aware
contrastive loss. Extensive experiments show that the linear evaluation of the
features learned by our proposed framework performs comparably with the fully
supervised training. Additionally, our framework shows high efficiency in the
few labeled data and transfer learning scenarios. The code is publicly
available at \url{https://github.com/emadeldeen24/CA-TCC}.
