Nonparametric Multiple Comparison Test for Change-Point Detection in Big Data

D. Klyushin, K. Golubeva
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引用次数: 2

Abstract

We offer a new effective tool for detection of change-points in tracking data (movement data, health rate data etc.). We developed a nonparametric test for homogeneity of data in two adjacent time intervals. In the context of Big Data and IoT it allows online analyzing data stream from sensor and recognizing significant deviations from the baseline. The significance level for the test is less than 0.05. Also, we provide the results of comparison of the test with well-known Kolmogorov–Smirnov test, the sign test, the Wilcoxon signed-rank test, and the Mann–Whitney test. The computational experiment has shown that the Klyushin–Petunin test based on p-statistics has very high robustness, specificity, and sensitivity, and is more universal than the Kolmogorov–Smirnov test, the sign test, the Wilcoxon signed-rank test, and the Mann–Whitney test.
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大数据变化点检测的非参数多重比较检验
我们提供了一种新的有效工具来检测跟踪数据(运动数据、健康率数据等)的变化点。我们在两个相邻的时间间隔中对数据的同质性进行了非参数检验。在大数据和物联网的背景下,它允许在线分析来自传感器的数据流并识别与基线的重大偏差。检验的显著性水平小于0.05。并与著名的Kolmogorov-Smirnov检验、符号检验、Wilcoxon符号秩检验和Mann-Whitney检验进行了比较。计算实验表明,基于p统计量的Klyushin-Petunin检验具有非常高的稳健性、特异性和敏感性,比Kolmogorov-Smirnov检验、符号检验、Wilcoxon符号秩检验和Mann-Whitney检验更具普适性。
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