Contrastive Learning of Data-Quality-Invariant Representations for Time Series Classification

Data quality issues are pervasive in time series collected from real-world sensing systems. Such issues can induce distribution shift and degrade downstream task performance. In this paper, we propose a self-supervised contrastive learning framework to learn data-quality-invariant representations for time series classification. First, we propose anomaly injection to generate negative samples for contrast with normal samples in the latent space, thereby mitigating data-quality-induced distribution shift. Second, we use autoencoder-based reconstruction as the pretext task to learn discriminative representations. Lastly, we freeze the pretrained backbone and employ a prototypical network for few-shot time series classification using a small number of labeled samples. Extensive experiments on real-world datasets demonstrate that our proposed model consistently outperforms state-of-the-art self-supervised contrastive learning frameworks, improving accuracy by an average of 4.09 percentage points across the three datasets compared with the strongest baseline on each dataset. An ablation study also shows that anomaly injection is effective at improving classification performance compared with current data augmentation techniques.