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.

翻译：时间序列异常检测（TSAD）是一个不断发展的研究领域，其驱动力在于其关键应用，例如检测地震活动、工业厂房中的传感器故障、预测股市崩盘等。在各个领域中，异常数据出现的频率显著低于正常数据，这使得F1分数成为异常检测中最常采用的评估指标。然而，在时间序列场景下，由于“时间点”与“时间事件”之间的分离，直接使用标准的F1分数并不直接。为了解决这个问题，在进行$F_1$分数评估之前，会对异常预测进行调整，这种方法被称为点调整（PA）。然而，这些调整是基于启发式规则的，并且偏向于真阳性检测，导致检测器性能被高估。在本工作中，我们提出了一种名为“平衡点调整”（BA）的替代调整方案。它解决了现有点调整方法的局限性，并基于TSAD评估的公理化定义提供了公平性保证。