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.

翻译：随着近年来技术的进步，各类系统持续收集大量随时间变化的数据，从而生成时间序列。时间序列异常检测（TSAD）在电子商务、网络安全、车辆维护和医疗健康监测等多种时间序列应用中具有重要意义。然而，该任务极具挑战性，因为它需要同时考虑时间序列数据中存在的变量内依赖（同一变量随时间变化的关系）和变量间依赖（多个变量之间的关系）。近期基于图的方法在该领域应对挑战方面取得了显著进展。本综述对使用图的时间序列异常检测（称为G-TSAD）进行了全面且最新的回顾。首先，我们探讨了图表示对时间序列数据的巨大潜力及其对促进异常检测的贡献。接着，我们回顾了在时间序列背景下最先进的图异常检测技术，这些技术大多利用深度学习架构。针对每种方法，我们讨论了其优势、局限性以及其擅长的具体应用场景。最后，我们阐述了该领域当前面临的技术与应用挑战，并提出了推动研究和改善实际成果的潜在未来方向。