论文标题
大规模检测的异常检测：深度分布时间序列模型的情况
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
论文作者
论文摘要
本文介绍了一种用于检测时间序列数据异常的新方法，并提供了监视（Micro-）服务和云资源的健康的主要应用。我们方法中的主要新颖性是，我们不是建模时间序列，而不是由真实值的真实值或向量组成，而是对实际值（或向量）的概率分布进行建模。该扩展到时间序列的概率分布，可以将技术应用于通用方案，在该方案中，通过到服务的请求生成数据，然后以固定的时间频率汇总。我们的方法适合流媒体检测和尺度以监测数百万个时间序列的异常。我们在合成和公共现实世界数据上显示了我们方法的卓越精度。在Yahoo WebScope数据集上，我们在4个数据集中的3个中胜过最新技术的状态，我们表明我们的表现优于流行的开源异常检测工具，可在现实世界中的数据集中最大17％的平均改进。
This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors). This extension to time series of probability distributions allows the technique to be applied to the common scenario where the data is generated by requests coming in to a service, which is then aggregated at a fixed temporal frequency. Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series. We show the superior accuracy of our method on synthetic and public real-world data. On the Yahoo Webscope data set, we outperform the state of the art in 3 out of 4 data sets and we show that we outperform popular open-source anomaly detection tools by up to 17% average improvement for a real-world data set.
