%0 Journal Article
%T 基于改进ADPP的多变量时间序列异常检测<br>Outlier detection based on improved ADPP for multivariate time series
%A 董红玉
%A 陈晓云
%J 福州大学学报(自然科学版)
%D 2016
%X 针对多变量时间序列异常检测问题进行研究，提出基于改进ADPP的多变量时间序列异常检测算法IADPP. IADPP算法引入适用于多变量时间序列的张量相似性度量 S SOTPCA，并以此相似性度量构造序列集的 k －近邻图，在构造的 k －近邻图上计算多变量时间序列的异常系数. 研究结果表明，IADPP算法克服了原有ADPP算法不支持多变量时间序列和要求密度均匀的缺陷，取得了较好的检测结果.<br>We study the outlier detection for multivariate time series，and an approach of outlier detection for multivariate time series based on improved ADPP-IADPP is proposed. IADPP algorithm introduces tensor similarity measure S SOTPCA supporting for multivariate time series，and constructs the k － neighbor graph about the sequence set. Then，we calculate the outlier coefficient of multivariate time series on k －neighbor graph .The research results show that the proposed method overcomes the disadvantages that original ADPP does not support multivariate time series and requests uniform density，IADPP algorithm achieves a better detection results
%K 多变量时间序列 异常检测 张量相似性度量 k －近邻图&lt
%K br&gt
%K multivariate time series outlier detection tensor similarity measure k －neighbor graph
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201602004&flag=1