论文标题
使用Quanttree中的多元数据集中的非参数和在线更改检测
Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree
论文作者
论文摘要
我们解决了多变量数据流中的在线变更检测问题，并介绍了Quanttree指数加权移动平均线（QT-EWMA），这是一种非参数变更检测算法，可以在误报之前控制预期的时间，从而产生所需的平均运行长度（ARL $ _0 $ _0 $）。在许多应用程序中，控制虚假警报至关重要，很少可以通过在线变更检测算法来保证，该算法可以监视多元数据串联而不知道数据分布。像许多变更检测算法一样，QT-EWMA从固定训练集中构建了数据分布的模型，在我们的情况下，量化的量化直方图。为了监视数据流，即使训练集非常小，我们提出了QT-Ewma-update，该QT-ewma-update在监视过程中会逐步更新Quanttree直方图，始终保持ARL $ _0 $控制。我们的实验在合成和现实世界的数据源上执行，证明QT-EWMA和QT-EWMA-UPDATE控制ARL $ _0 $和错误的警报率比在类似条件下运行的最先进的方法更好，从而实现了较低或可比的检测延迟。
We address the problem of online change detection in multivariate datastreams, and we introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric change-detection algorithm that can control the expected time before a false alarm, yielding a desired Average Run Length (ARL$_0$). Controlling false alarms is crucial in many applications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams without knowing the data distribution. Like many change-detection algorithms, QT-EWMA builds a model of the data distribution, in our case a QuantTree histogram, from a stationary training set. To monitor datastreams even when the training set is extremely small, we propose QT-EWMA-update, which incrementally updates the QuantTree histogram during monitoring, always keeping the ARL$_0$ under control. Our experiments, performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and QT-EWMA-update control the ARL$_0$ and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving lower or comparable detection delays.
