在流式世界中，你是否应该静止不动？流式异常检测的综合基准
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
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中文总结 AI 辅助
本研究通过大规模统一基准比较流式与静态时间序列异常检测方法，发现静态方法在多数流式场景中显著更优，并提出了分布漂移数据集TSB-drift，揭示现有流式方法设计与实际需求间的关键差距。
中文摘要 AI 辅助
时间序列异常检测（TSAD）越来越多地部署在流式环境中，其中数据按顺序到达并可能表现出非平稳性。因此，近期文献中的多项工作提出了依赖于增量更新以随时间适应的流式异常检测方法。然而，这些方法大多源自流式离群点检测文献，并在很大程度上忽略了时间序列异常的核心特征。此外，其实证评估通常是在合成或小规模且多样性有限的基准上进行的，这使得流式方法在现实TSAD场景中是否真正具有优势尚不明确。在本工作中，我们进行了首次大规模实验研究，在统一的流式评估基准下比较流式与静态TSAD方法。我们考虑了一个现实设置：初始数据批次可用于模型训练，随后对检测准确性和计算效率进行在线评估。此外，我们提出了一个真实时间序列的分布漂移数据集，称为TSB-drift，以隔离流式更新在理论上合理的场景。我们的结果表明，与常见假设相反，静态TSAD方法在大多数流式设置中显著优于流式方法。这一发现凸显了现有流式方法设计与现代TSAD需求之间的关键差距，并呼吁重新思考如何将流式能力整合到TSAD中。
英文摘要
Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection methods that rely on incremental updates to adapt over time. However, most of these approaches originate from the streaming outlier detection literature and largely ignore core characteristics of time series anomalies. Moreover, their empirical evaluation is typically conducted on synthetic or small-scale benchmarks with limited diversity, making it unclear whether streaming methods are truly advantageous in realistic TSAD scenarios. In this work, we carry out the first large-scale experimental study comparing streaming and static TSAD methods under a unified streaming evaluation benchmark. We consider a realistic setting in which an initial batch of data is available for model training, followed by online evaluation of both detection accuracy and computational efficiency. In addition, we propose a distribution-drift dataset of real time series, called TSB- drift, to isolate scenarios where streaming updates are theoretically justified. Our results show that, contrary to common assumptions, static TSAD methods significantly outperform streaming approaches in most streaming settings. Such finding highlights a critical gap between the design of existing streaming methods and the requirements of modern TSAD, and calls for a rethinking of how streaming capabilities should be integrated into TSAD.
发表机构
- EDF R&D（法国电力集团研发部）
- ENS, PSL University, CNRS, Inria（巴黎高等师范学院，巴黎文理研究大学，法国国家科学研究中心，法国国家信息与自动化研究所）
- DI ENS, ENS, PSL University, CNRS, Inria（巴黎高等师范学院计算机科学系，巴黎高等师范学院，巴黎文理研究大学，法国国家科学研究中心，法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。
