Time series self-supervised learning (SSL) aims to exploit unlabeled data for
pre-training to mitigate the reliance on labels. Despite the great success in
recent years, there is limited discussion on the potential noise in the time
series, which can severely impair the performance of existing SSL methods. To
mitigate the noise, the de facto strategy is to apply conventional denoising
methods before model training. However, this pre-processing approach may not
fully eliminate the effect of noise in SSL for two reasons: (i) the diverse
types of noise in time series make it difficult to automatically determine
suitable denoising methods; (ii) noise can be amplified after mapping raw data
into latent space. In this paper, we propose denoising-aware contrastive
learning (DECL), which uses contrastive learning objectives to mitigate the
noise in the representation and automatically selects suitable denoising
methods for every sample. Extensive experiments on various datasets verify the
effectiveness of our method. The code is open-sourced.

时间序列自监督学习的噪声消除方法及其对比学习目标选择适合的降噪方法，以提高表示的准确性。