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.

我们的研究论文关注于强大的时间序列表示学习，通过两种不同编码器为输入的时间序列创建两个视图，并通过协同训练的对比学习来学习这些编码器，通过利用不同视图的互补信息，我们提出的TS-CoT方法可以减缓数据噪声和损坏的影响，通过精细调节，TS-CoT学习到的表示可以很好地转移到下游任务中。

噪声时间序列学习的联合训练方法