A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Zhenyu Zhong Qiliang Fan Jiacheng Zhang Minghua Ma Shenglin Zhang Yongqian Sun Qingwei Lin Yuzhi Zhang Dan Pei
Internet-based services have seen remarkable success, generating vast amounts
of monitored key performance indicators (KPIs) as univariate or multivariate
time series. Monitoring and analyzing these time series are crucial for
researchers, service operators, and on-call engineers to detect outliers or
anomalies indicating service failures or significant events. Numerous advanced
anomaly detection methods have emerged to address availability and performance
issues. This review offers a comprehensive overview of time series anomaly
detection in Artificial Intelligence for IT operations (AIOps), which uses AI
capabilities to automate and optimize operational workflows. Additionally, it
explores future directions for real-world and next-generation time-series
anomaly detection based on recent advancements.