CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification
This work addresses the problem of improving self-supervised learning for multivariate time series classification, which is incremental as it builds on existing methods by enhancing encoder design and pretext tasks.
The paper tackles the challenge of self-supervised representation learning for multivariate time series by proposing CaSS, a framework that combines a channel-aware Transformer encoder with novel pretext tasks, achieving state-of-the-art results with up to a 7.70% improvement on the LSST dataset.
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.