A Co-training Approach for Noisy Time Series Learning
Weiqi Zhang Jianfeng Zhang Jia Li Fugee Tsung
In this work, we focus on robust time series representation learning. Our
assumption is that real-world time series is noisy and complementary
information from different views of the same time series plays an important
role while analyzing noisy input. Based on this, we create two views for the
input time series through two different encoders. We conduct co-training based
contrastive learning iteratively to learn the encoders. Our experiments
demonstrate that this co-training approach leads to a significant improvement
in performance. Especially, by leveraging the complementary information from
different views, our proposed TS-CoT method can mitigate the impact of data
noise and corruption. Empirical evaluations on four time series benchmarks in
unsupervised and semi-supervised settings reveal that TS-CoT outperforms
existing methods. Furthermore, the representations learned by TS-CoT can
transfer well to downstream tasks through fine-tuning.