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 and the inter-variable dependency, where a variable can be defined as an observation 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 Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.

翻译：随着近期技术的发展，各类系统持续采集海量时序数据并生成时间序列。时间序列异常检测（TSAD）是电子商务、网络安全、车辆维护和医疗监测等时序应用中的重要任务。然而，该任务极具挑战性，因其需同时考虑变量内部依赖性与变量间依赖性（其中变量可定义为时序数据中的观测值）。近年来，基于图的方法在攻克该领域难题方面取得了显著进展。本综述对基于图的时间序列异常检测（G-TSAD）进行了全面且最新的回顾。首先，我们探讨了图表示学习在时序数据中的巨大潜力；随后，梳理了时序背景下最先进的图异常检测技术，并分析其优势与不足；最后，讨论了该研究领域的技术挑战及未来可能的改进方向。