梳理近十年时间序列异常检测方法，揭示研究趋势与核心思路。
Dive into Time-Series Anomaly Detection: A Decade Review
- 按处理流程分类现有异常检测方法，构建系统性框架。
- 通过元分析总结主流技术演进路径与性能表现规律。
- 适合关注时序分析、工业监测与安全预警的科研人员参考。
近年来，数据采集技术的进展以及流式数据量和速度的持续增长，凸显了时间序列分析的重要性。在此背景下，时间序列异常检测成为关键任务，广泛应用于网络安全、金融市场、执法和医疗等领域。尽管传统异常检测多基于统计方法，但近年来机器学习算法的激增要求对时间序列异常检测的研究方法进行系统化、通用化的归纳。本文基于流程中心的分类体系，对现有解决方案进行分组与总结。除了提出原创性的方法分类外，还开展文献元分析，梳理时间序列异常检测研究的整体发展趋势。
原文摘要 · Abstract (English)
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.
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