Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models

Thi Kieu Khanh Ho, Narges Armanfard

Keywords: Time-series Anomaly Detection, Contaminated Data, Graphs, Diffusion Models

TL;DR: In this paper, we introduce a novel and practical end-to-end unsupervised time-series anomaly detection when the training data is contaminated with anomalies.

Abstract: Mainstream unsupervised anomaly detection algorithms often excel in academic datasets, yet their real-world performance is restricted due to the controlled experimental conditions involving clean training data. Addressing the challenge of training with noise, a prevalent issue in practical anomaly detection, is frequently overlooked. In a pioneering endeavor, this study delves into the realm of label-level noise within sensory time-series anomaly detection (TSAD). This paper presents a novel and practical TSAD when the training data is contaminated with anomalies. The introduced approach, called TSAD-C, is devoid of access to abnormality labels during the training phase. TSAD-C encompasses three modules: a Decontaminator to rectify anomalies present during training and swiftly prepare the decontaminated data for subsequent modules; a Long-range Variable Dependency Modeling module to capture long-range intra- and inter-variable dependencies within the decontaminated data that is considered as a surrogate of the pure normal data; and an Anomaly Scoring module that leverages insights of the first two modules to detect all types of anomalies. Our extensive experiments conducted on four reliable, diverse, and challenging datasets conclusively demonstrate that TSAD-C surpasses existing methods, thus establishing a new state-of-the-art in the TSAD field.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission109/Authors, auai.org/UAI/2025/Conference/Submission109/Reproducibility_Reviewers

Submission Number: 109