Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these samples. Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques, aiming to preserve temporal patterns against perturbations for MTS data. However, they overlook spatial consistency that requires the stability of individual sensors and their correlations. As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data. Thus, we propose Graph Contextual Contrasting (GCC) for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations to preserve the stability of sensors and their correlations, followed by graph contrasting with both node- and graph-level contrasting to extract robust sensor- and global-level features. We further introduce multi-window temporal contrasting to ensure temporal consistency in the data for each sensor. Extensive experiments demonstrate that our proposed GCC achieves state-of-the-art performance on various MTS classification tasks.

翻译：对比学习作为一种自监督学习范式，在多元时间序列分类中日益流行。它通过确保未标注样本在不同视图间的一致性，从而学习这些样本的有效表征。现有对比学习方法主要关注利用时间增强和对比技术实现时间一致性，旨在保留多元时间序列数据在扰动下的时间模式。然而，这些方法忽视了空间一致性——即要求各传感器及其关联的稳定性。由于多元时间序列数据通常源自多个传感器，确保空间一致性对于多元时间序列数据上对比学习的整体性能至关重要。为此，我们提出图上下文对比学习以实现多元时间序列数据的空间一致性。具体而言，我们设计了包括节点增强和边增强在内的图增强方法，以保持传感器及其关联的稳定性；随后通过节点级和图级对比的图对比学习，提取鲁棒的传感器级和全局级特征。进一步，我们引入多窗口时间对比，确保每个传感器数据的时间一致性。大量实验表明，我们提出的图上下文对比学习在多种多元时间序列分类任务上达到了最优性能。