| 多变量时间序列滑动窗口异常点的检测 |
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| 引用本文： | 戴慧,阚建飞,李伟仁,周卫东.多变量时间序列滑动窗口异常点的检测[J].南京气象学院学报,2014,6(6):515-519. |
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| 作者姓名： | 戴慧 阚建飞 李伟仁 周卫东 |
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| 作者单位： | 南京工程学院 计算机工程学院, 南京, 210013;南京工程学院 计算机工程学院, 南京, 210013;德州大学阿灵顿分校 电子工程系, 达拉斯, 美国, 76013;德州大学阿灵顿分校 电子工程系, 达拉斯, 美国, 76013 |
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| 基金项目： | 江苏省政府留学奖学金基金;国家自然科学基金(61103141);江苏省高校自然科学基金(13KJB520015) |
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| 摘 要： | 针对多变量时间序列(MTS)的异常点的探测问题,提出了采用由粗到细的二次探测方案.基于滑动窗口数据的置信区间,构造了变化趋势值特征和相对变化趋势值特征分别用于二次探测,同时研究了特征的快速提取算法.通过对OPEN3000数据监测系统采集的事故发生前后某市城南变电站各设备表的数据集进行异常点探测,结果表明提出的算法能够快速准确地探测出异常点的位置.
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| 关 键 词： | 多变量时间序列 滑动窗口 异常点 置信区间 |
| 收稿时间： | 2014/9/6 0:00:00 |
Outlier detection for sliding window of multi-variable time series |
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| DAI Hui,KAN Jianfei,LEE Weiren and ZHOU Weidong.Outlier detection for sliding window of multi-variable time series[J].Journal of Nanjing Institute of Meteorology,2014,6(6):515-519. |
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| Authors: | DAI Hui KAN Jianfei LEE Weiren and ZHOU Weidong |
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| Institution: | School of Computer Engineering, Nanjing Institute of Technology, Nanjing 210013;School of Computer Engineering, Nanjing Institute of Technology, Nanjing 210013;Department of Electronic Engineering, University of Texas in Arlington, Dallas 76013;Department of Electronic Engineering, University of Texas in Arlington, Dallas 76013 |
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| Abstract: | This paper proposes a two-step detection scheme that begins thick and ends thin,to mine the outliers of multivariable time series (MTS).According to the confidence interval of the data in sliding window,characteristics of both variation trend value and relevant variation trend value were constructed,which were then used in the two detection processes.Meanwhile,the rapid extraction algorithm for characteristics is studied.The outlier detection scheme is then applied to mine outliers before and after an accident happened at a 110 kV Grid Transformer Substation in Jiangsu province.Data sets of various equipment tables,which were collected by OPEN3000 data surveillance system,were checked by the proposed detection scheme,and experiment result indicates that this algorithm can rapidly and precisely locate the outliers. |
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| Keywords: | multivariate time series sliding window outliers confidence interval |
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