The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time-series research as outlier and novelty detection or time-series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios. To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROC, a generalisation for the calculation of ROC curves for time-series scenarios. We also adapt the mechanism from a established time-series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big Data algorithms with a real-world time-series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method.
翻译:异常点检测研究缺乏对异常点的一致定义。异常点本身的性质本身的差别导致算法设计和实验的多重范式。预测性维护是一个特殊情况,异常点代表了必须防止的失败。相关的时间序列研究作为异端和新颖的检测或时间序列分类,并不适用于这一领域的异常点概念,因为它们不是以前未曾见过的单一点,也可能不是确切的附加说明的异常点。此外,由于缺乏附加说明的异常点数据,许多基准是从受监督的情景中调整的。为了解决这些问题,我们推广了正反两种情况的概念,以便每隔一段时间评价不受监督的异常检测算法。我们还保留了不平衡性评价计划,即通过退缩窗口ROC的建议,即计算时间序列情景的ROC曲线的概括性计算。我们还将机制从既定的时间序列异常检测基准调整为拟议的概括性基准,以奖励早期检测。因此,该提案代表了对不同情景的灵活评估框架。为了应对不同情景,我们推广了对正反向情况进行定期评估,我们提供了一种比较的方法,将数据序列的系统与公司模型进行比较。