Time-series representation learning can extract representations from data with temporal dynamics and sparse labels. When labeled data are sparse but unlabeled data are abundant, contrastive learning, i.e., a framework to learn a latent space where similar samples are close to each other while dissimilar ones are far from each other, has shown outstanding performance. This strategy can encourage varied consistency of time-series representations depending on the positive pair selection and contrastive loss. We propose a new time-series representation learning method by combining the advantages of self-supervised tasks related to contextual, temporal, and transformation consistency. It allows the network to learn general representations for various downstream tasks and domains. Specifically, we first adopt data preprocessing to generate positive and negative pairs for each self-supervised task. The model then performs contextual, temporal, and transformation contrastive learning and is optimized jointly using their contrastive losses. We further investigate an uncertainty weighting approach to enable effective multi-task learning by considering the contribution of each consistency. We evaluate the proposed framework on three downstream tasks: time-series classification, forecasting, and anomaly detection. Experimental results show that our method not only outperforms the benchmark models on these downstream tasks, but also shows efficiency in cross-domain transfer learning.

翻译：时间序列表示学习可以从具有时间动态性和稀疏标签的数据中提取表示。当标注数据稀疏而未标注数据丰富时，对比学习（即通过一个学习潜在空间的框架，使相似样本之间距离近，不相似样本之间距离远）展现出卓越性能。该策略可根据正样本对选择方式和对比损失函数，促进时间序列表示的多重一致性。我们提出了一种新颖的时间序列表示学习方法，通过融合与上下文一致性、时间一致性和变换一致性相关的自监督任务优势，使得网络能够为各种下游任务和领域学习通用表示。具体而言，我们首先采用数据预处理为每个自监督任务生成正负样本对，随后模型分别进行上下文对比学习、时间对比学习和变换对比学习，并通过联合优化各对比损失函数来训练模型。我们进一步引入不确定性加权方法，通过考虑每种一致性的贡献以实现有效的多任务学习。在时间序列分类、预测和异常检测三个下游任务上的评估结果表明，我们的方法不仅在下游任务上优于基准模型，在跨域迁移学习中也展现出高效性。