  Self-supervised representation learning of Multivariate Time Series (MTS) is
a challenging task and attracts increasing research interests in recent years.
Many previous works focus on the pretext task of self-supervised learning and
usually neglect the complex problem of MTS encoding, leading to unpromising
results. In this paper, we tackle this challenge from two aspects: encoder and
pretext task, and propose a unified channel-aware self-supervised learning
framework CaSS. Specifically, we first design a new Transformer-based encoder
Channel-aware Transformer (CaT) to capture the complex relationships between
different time channels of MTS. Second, we combine two novel pretext tasks Next
Trend Prediction (NTP) and Contextual Similarity (CS) for the self-supervised
representation learning with our proposed encoder. Extensive experiments are
conducted on several commonly used benchmark datasets. The experimental results
show that our framework achieves new state-of-the-art comparing with previous
self-supervised MTS representation learning methods (up to +7.70\% improvement
on LSST dataset) and can be well applied to the downstream MTS classification.
