Online anomaly detection on the webscope S5 dataset: A comparative study

机译：Webscope S5数据集上的在线异常检测：一项比较研究

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An unresolved challenge for all kind of temporal data is the reliable anomaly detection, especially when adaptability is required in the case of non-stationary time series or when the nature of future anomalies is unknown or only vaguely defined. Most of the current anomaly detection algorithms follow the general idea to classify an anomaly as a significant deviation from the prediction. In this paper we present a comparative study where several online anomaly detection algorithms are compared on the large Yahoo Webscope S5 anomaly benchmark. We show that a relatively Simple Online Regression Anomaly Detector (SORAD) is quite successful compared to other anomaly detectors. We discuss the importance of several adaptive and online elements of the algorithm and their influence on the overall anomaly detection accuracy.

机译：对于所有类型的时间数据，尚未解决的挑战是可靠的异常检测，尤其是在非平稳时间序列的情况下需要适应性时，或者当未来异常的性质未知或仅模糊地定义时。当前大多数异常检测算法都遵循一般思想，将异常归类为与预测的显着偏差。在本文中，我们提供了一项比较研究，其中在大型Yahoo Webscope S5异常基准上比较了几种在线异常检测算法。我们显示，与其他异常检测器相比，相对简单的在线回归异常检测器（SORAD）相当成功。我们讨论了算法的几个自适应和在线元素的重要性及其对整体异常检测精度的影响。

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来源
《2017 Evolving and Adaptive Intelligent Systems》|2017年|1-8|共8页
会议地点 Ljubljana(SI)
作者
Markus Thill; Wolfgang Konen; Thomas Bäck;
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作者单位

Department of Computer Science, Cologne University of Applied Sciences, 51643 Gummersbach, Germany;

Department of Computer Science, Cologne University of Applied Sciences, 51643 Gummersbach, Germany;

Department of Computer Science, Leiden University, LIACS, 2333 CA Leiden, The Netherlands;

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原文格式 PDF
正文语种 eng
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关键词
Time series analysis; Prediction algorithms; Benchmark testing; Detection algorithms; Training; Training data; Transient analysis;

机译：时间序列分析;预测算法;基准测试;检测算法;训练;训练数据;瞬态分析;

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Online anomaly detection on the webscope S5 dataset: A comparative study

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