aiqbench analysis
This study challenges the conventional wisdom that streaming anomaly detection methods are superior by demonstrating that static models often outperform incremental approaches in realistic, non-stationary time series scenarios. The authors introduce a novel distribution-drift dataset to rigorously evaluate these methods under conditions where data characteristics change over time. Their findings suggest a critical need to reconsider how adaptive capabilities are integrated into modern anomaly detection architectures.
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
arXiv:2609.39215v1 Announce Type: cross Abstract: 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.
Title:In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.39215 [cs.LG]
(or arXiv:2609.39215v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.39215 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2026
https://doi.org/10.1145/3770855.3817495 Focus to learn more DOI(s) linking to related resources
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