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. The code is available at https://github.com/Frank-Wang-oss/TS-GAC.

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。它通过确保未标记样本不同视图间的一致性，学习这些样本的有效表示。现有对比学习方法主要关注通过时间增强和对比技术实现时间一致性，旨在对多元时间序列数据保留其针对扰动的时序模式。然而，这些方法忽略了要求单个传感器及其关联稳定性的空间一致性。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对比学习在多元时间序列数据上的整体性能至关重要。为此，我们提出面向多元时间序列数据空间一致性的图感知对比学习。具体而言，我们提出包含节点增强和边增强的图增强方法，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学习提取鲁棒的传感器级和全局级特征。此外，我们引入多窗口时间对比学习，确保每个传感器数据的时间一致性。大量实验表明，我们提出的方法在多种多元时间序列分类任务中均取得了最先进的性能。代码已开源至 https://github.com/Frank-Wang-oss/TS-GAC。