提出一套形式化评估标准，解决时序异常检测指标不一致问题
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
- 构建可验证的理论性质，定义评估异常检测的核心要求
- 分析37个常用指标，发现无一满足全部性质，解释历史结果矛盾
- 提出LARM和ALARM新指标，确保理论完备性，适合严谨对比研究
时间序列中的未检测异常可能引发化工厂爆炸或电网瘫痪等灾难性后果。尽管已有众多检测方法，但其性能评估仍不清晰，因现有指标仅捕捉任务的片面特征，常导致误导性结果。本文通过引入可验证的性质，形式化时序异常检测评估的基本要求，建立理论框架以支持合理评估与可靠比较。分析37个广泛应用的指标后发现，多数仅满足少数性质，且无一满足全部，解释了以往结果的持续不一致性。为填补这一差距，我们提出LARM——一种可证明满足所有性质的灵活指标，并进一步扩展为满足更严格要求的ALARM变体。
原文摘要 · Abstract (English)
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection methods have been proposed, their performance remains unclear because current metrics capture only narrow aspects of the task and often yield misleading results. We address this issue by introducing verifiable properties that formalize essential requirements for evaluating time-series anomaly detection. These properties enable a theoretical framework that supports principled evaluations and reliable comparisons. Analyzing 37 widely used metrics, we show that most satisfy only a few properties, and none satisfy all, explaining persistent inconsistencies in prior results. To close this gap, we propose LARM, a flexible metric that provably satisfies all properties, and extend it to ALARM, an advanced variant meeting stricter requirements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。