Graph Anomaly Detection in Time Series: A Survey

2023/01/31 by Thi Kieu Khanh Ho, Ho, Thi Kieu Khanh, Ali Karami +3 · 6 citations
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2302.00058

openalex publication_date 2023/01/31 · openalex created_date 2023/02/04 · openalex updated_date 2026/07/28

Abstract

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency (relationships within a variable over time) and the inter-variable dependency (relationships between multiple variables) existing in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation for time-series data and and its contributions to facilitating anomaly detection. Then, we review state-of-the-art graph anomaly detection techniques, mostly leveraging deep learning architectures, in the context of time series. For each method, we discuss its strengths, limitations, and the specific applications where it excels. Finally, we address both the technical and application challenges currently facing the field, and suggest potential future directions for advancing research and improving practical outcomes.

Cited by

• Anomaly Detection and Generation with Diffusion Models: A Survey
• Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond
• CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
• Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
• mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
• HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection

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