Nonparametric Monitoring of Data Streams for Changes in Location and Scale
Nonparametric Monitoring of Data Streams for Changes in Location and Scale
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DOI：
10.1198/tech.2011.10069
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发表时间：
2011-11-01
期刊：
影响因子：
2.5
通讯作者：
Adams, Niall M.
中科院分区：
文献类型：
--
作者：
Ross, Gordon J.;Tasoulis, Dimitris K.;Adams, Niall M.
数据流的分析需要能够处理大量数据点的方法。在算法必须具有恒定的计算复杂度和固定内存量的要求下，当流变量的分布形式未知时，我们开发了一个用于检测数据流变化的框架。我们考虑检测随机变量流的位置和/或尺度参数变化的一般问题，并调整了几种非参数假设检验来创建一种流变化检测算法。该算法使用一种检验统计量，其零分布与数据无关。这使得即使在数据流分布未知的情况下，对于任何数据流都能保持期望的误报率。我们的方法基于涉及对数据点排序的假设检验，并且我们提出了一种以符合数据流分析约束的方式在线计算这些秩的方法。
The analysis of data streams requires methods which can cope with a very high volume of data points. Under the requirement that algorithms must have constant computational complexity and a fixed amount of memory, we develop a framework for detecting changes in data streams when the distributional form of the stream variables is unknown. We consider the general problem of detecting a change in the location and/or scale parameter of a stream of random variables, and adapt several nonparametric hypothesis tests to create a streaming change detection algorithm. This algorithm uses a test statistic with a null distribution independent of the data. This allows a desired rate of false alarms to be maintained for any stream even when its distribution is unknown. Our method is based on hypothesis tests which involve ranking data points, and we propose a method for calculating these ranks online in a manner which respects the constraints of data stream analysis.