Abstract This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system’s typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy nonstationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the online supplementary materials.

中文翻译：

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流式非平稳时间数据中的异常检测

摘要 本文提出了一个框架，该框架可在大量非平稳流式时间序列数据中提供异常序列的早期检测。我们将异常定义为观察，也就是说，鉴于给定系统的近期分布，这种异常非常不可能。所提出的框架首先使用极值理论计算系统典型行为的边界。然后使用滑动窗口来测试新到达的系列集合中的异常系列。该模型使用时间序列特征作为输入，并通过基于密度的比较来检测特征分布的任何显着变化。使用各种合成和现实世界的数据集，我们证明了我们提出的框架的广泛适用性和实用性。我们表明，在多类时间序列中存在噪声非平稳数据时，所提出的算法可以很好地工作。该框架在开源 R 包oddstream 中实现。R 代码和数据可在在线补充材料中找到。