提出公平评估时间序列异常检测的新方法
Towards Unbiased Evaluation of Time-series Anomaly Detector
- 设计平衡点调整法，避免传统方法对真阳性偏倚
- 基于公理化定义确保评估结果无系统性偏差
- 适合需可靠性能比较的工业异常检测场景
时间序列异常检测（TSAD）在地震监测、工业传感器故障预警、股市崩盘预测等关键领域具有重要应用。由于异常事件远少于正常数据，通常采用F1分数作为评估指标。然而，时间序列中‘时间点’与‘异常事件’存在分离，导致标准F1分数难以直接使用。现有方法通过点调整（PA）进行修正，但这类调整依赖启发式规则，偏向高召回率，造成性能评估虚高。本文提出一种新的平衡点调整（BA）协议，克服了现有方法的局限性，其公平性由时间序列异常检测评估的公理化定义保障。
原文摘要 · Abstract (English)
Time series anomaly detection (TSAD) is an evolving area of research motivated by its critical applications, such as detecting seismic activity, sensor failures in industrial plants, predicting crashes in the stock market, and so on. Across domains, anomalies occur significantly less frequently than normal data, making the F1-score the most commonly adopted metric for anomaly detection. However, in the case of time series, it is not straightforward to use standard F1-score because of the dissociation between `time points' and `time events'. To accommodate this, anomaly predictions are adjusted, called as point adjustment (PA), before the $F_1$-score evaluation. However, these adjustments are heuristics-based, and biased towards true positive detection, resulting in over-estimated detector performance. In this work, we propose an alternative adjustment protocol called ``Balanced point adjustment'' (BA). It addresses the limitations of existing point adjustment methods and provides guarantees of fairness backed by axiomatic definitions of TSAD evaluation.
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