Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.

本研究解决了时间序列异常检测领域亟需系统化的研究方法的不足。通过建立过程中心的分类法，该论文对现有的异常检测解决方案进行了分组和总结，并进行了文献的元分析，揭示了时间序列异常检测研究的一般趋势。研究的最显著发现是对现有方法的原始分类，为相关领域提供了新的视角。

深入探讨时间序列异常检测：十年回顾