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-Aware Contrasting 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 method 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，在多变量时间序列分类任务中日益流行。该范式通过确保未标注样本在不同视图下的一致性，进而学习样本的有效表征。现有对比学习方法主要关注时间一致性——利用时间增强与对比技术，旨在使多变量时间序列数据在扰动下保持时序模式。然而，这些方法忽视了空间一致性，即要求单个传感器及其相关性的稳定性。由于多变量时间序列数据通常源自多个传感器，确保空间一致性对于提升对比学习在多变量时间序列数据上的整体性能至关重要。为此，我们提出图感知对比方法以实现多变量时间序列数据的空间一致性。具体而言，我们设计了包含节点增强和边增强的图增强策略，以保持传感器及其相关性的稳定性；随后通过节点级和图级双重图对比，提取鲁棒的传感器级与全局级特征。此外，我们引入多窗口时间对比机制，确保每个传感器数据中的时间一致性。大量实验表明，所提方法在各种多变量时间序列分类任务上均取得了最优性能。