MNP-GDA: Multimodal Normality Prototypical Graph Domain Adaptation for Multivariate Time Series Anomaly Detection

Traditional multivariate time series anomaly detection (MTS-AD) methods are primarily developed under the single-domain assumption that both training and test data originate from the same domain, while this rarely holds in real-world applications. The newly emerging cross-domain MTS-AD problem aims to transfer knowledge from a labeled source domain to assist MTS-AD in an unlabeled target domain. However, how to explicitly model spatio-temporal dependencies and transfer multimodal normalities across heterogeneous MTS domains remains largely under-explored. To address this, we propose a Multimodal Normality Prototypical Graph Domain Adaptation (MNP-GDA) framework that captures latent spatio-temporal dependencies by adaptive graph modeling. Furthermore, MNP-GDA introduces a prototypical domain adaptation module with theoretical justification, which models and transfers diverse normal modes across domains upon multiple normal prototypes, by compacting normal samples around their nearest prototypes while separating anomalies from all prototypes. Finally, a label-conditioned adaptive reliability weighting mechanism supported by theoretical analysis is devised to weight pseudo-normal and -anomalous target samples oppositely, mitigating negative influence of unreliable pseudo-labels during domain adaptation. Extensive experiments on real-world server-monitoring and industrial-control benchmark datasets demonstrate state-of-the-art performance of MNP-GDA on cross-domain MTS-AD tasks.