一种可控制误报率的多变量数据流在线变化检测方法
Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree
- 结合核量化树与EWMA统计量实现非参数化在线监测
- 在预设平均运行长度下稳定控制误报率，检测延迟优于或持平主流方法
- 适合对误报敏感的实时监控场景，如工业异常检测
我们提出Kernel-QuantTree指数加权移动平均（KQT-EWMA）算法，一种非参数化的多变量数据流在线变化检测方法。该方法融合核量化树（KQT）直方图与EWMA统计量，可建模任意平稳分布，且在平稳状态下检验统计量分布不依赖于数据分布，实现非参数化监控。通过设定预定义的平均运行长度（ARL₀，即触发一次误报前需监测的平稳样本期望数量），可精确控制误报率。这一特性不同于多数非参数检测方法，后者通常无法事前控制ARL₀。在合成与真实数据集上的实验表明，KQT-EWMA在保持良好误报控制能力的同时，检测延迟与当前最优方法相当或更优。
原文摘要 · Abstract (English)
We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic to monitor multivariate data streams online. The resulting monitoring scheme is very flexible, since histograms can be used to model any stationary distribution, and practical, since the distribution of test statistics does not depend on the distribution of datastream in stationary conditions (non-parametric monitoring). KQT-EWMA enables controlling false alarms by operating at a pre-determined Average Run Length ($ARL_0$), which measures the expected number of stationary samples to be monitored before triggering a false alarm. The latter peculiarity is in contrast with most non-parametric change-detection tests, which rarely can control the $ARL_0$ a priori. Our experiments on synthetic and real-world datasets demonstrate that KQT-EWMA can control $ARL_0$ while achieving detection delays comparable to or lower than state-of-the-art methods designed to work in the same conditions.
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