CAJ | 학술논문

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。
침대다변량시간서렬（ MTS）적이상점적탐측문제，제출료채용유조도세적이차탐측방안。기우활동창구수거적치신구간，구조료변화추세치특정화상대변화추세치특정분별용우이차탐측，동시연구료특정적쾌속제취산법。통과대OPEN3000수거감측계통채집적사고발생전후모시성남변전참각설비표적수거집진행이상점탐측，결과표명제출적산법능구쾌속준학지탐측출이상점적위치。
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 Substa?tion 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 rap?idly and precisely locate the outliers.