Time series anomaly detection has been a perennially important topic in data science, with papers dating back to the 1950s. However, in recent years there has been an explosion of interest in this topic, much of it driven by the success of deep learning in other domains and for other time series tasks. Most of these papers test on one or more of a handful of popular benchmark datasets, created by Yahoo, Numenta, NASA, etc. In this work we make a surprising claim. The majority of the individual exemplars in these datasets suffer from one or more of four flaws. Because of these four flaws, we believe that many published comparisons of anomaly detection algorithms may be unreliable, and more importantly, much of the apparent progress in recent years may be illusionary. In addition to demonstrating these claims, with this paper we introduce the UCR Time Series Anomaly Archive. We believe that this resource will perform a similar role as the UCR Time Series Classification Archive, by providing the community with a benchmark that allows meaningful comparisons between approaches and a meaningful gauge of overall progress.
翻译:时间序列异常现象探测是数据科学中一个常年重要话题,论文可追溯到1950年代。然而,近年来,人们对这个话题的兴趣大增,其中很大一部分是由于其他领域和其他时间序列任务中深层学习的成功所推动的。这些论文大多是对亚胡、努门塔、美国航天局等亚虎、努门塔、美国航天局制作的少数流行基准数据集中的一个或数个进行测试。在这项工作中,我们提出一个令人惊讶的要求。这些数据集中的大多数个人模型都存在一个或四个以上的缺陷。由于这四个缺陷,我们认为,许多公布的异常检测算法比较可能不可靠,更重要的是,近年来许多明显的进展可能是幻想性的。除了展示这些说法外,我们介绍UCR Time系列阿诺玛利档案。我们相信,这一资源将发挥类似于UCR Time系列分类档案的类似作用,为社区提供一个基准,使其能够对各种方法进行有意义的比较,并对总体进展进行有意义的衡量。