Recent algorithms of time-series anomaly detection have been evaluated by applying a Point Adjustment (PA) protocol. However, the PA protocol has a problem of overestimating the performance of the detection algorithms because it only depends on the number of detected abnormal segments and their size. We propose a novel evaluation protocol called the Point-Adjusted protocol with decay function (PAdf) to evaluate the time-series anomaly detection algorithm by reflecting the following ideal requirements: detect anomalies quickly and accurately without false alarms. This paper theoretically and experimentally shows that the PAdf protocol solves the over- and under-estimation problems of existing protocols such as PA and PA\%K. By conducting re-evaluations of SOTA models in benchmark datasets, we show that the PA protocol only focuses on finding many anomalous segments, whereas the score of the PAdf protocol considers not only finding many segments but also detecting anomalies quickly without delay.

翻译：最近的时间序列异常检测算法通常通过应用点调整(PA)协议进行评估。然而，PA协议仅依赖于检测到的异常分段数量及其大小，导致对检测算法性能的高估问题。本文提出一种新型评估协议——带衰减函数的点调整协议(PAdf)，通过满足以下理想需求来评估时间序列异常检测算法：在无虚警的前提下快速且准确地检测异常。本文从理论和实验角度证明，PAdf协议解决了PA和PA%K等现有协议的高估与低估问题。通过对基准数据集上的SOTA模型进行重新评估，我们表明PA协议仅侧重于发现更多异常分段，而PAdf协议的评分不仅考虑发现更多分段，还考虑无延迟地快速检测异常。