SPAR:set-based piecewise aggregate representation for time series anomaly detection  

作　　者：Peng ZHAN Yupeng HU Lin CHEN Wei LUO Xueqing LI 

机构地区：[1]School of Software,Shandong Vniversity,Jinan 250100,China [2]School of Computer Science and Technology,Shandong University,Qingdao 266237,China [3]Inforrnatization Office,Shandong University,Jinan 250100,China

出　　处：《Science China(Information Sciences)》2021年第4期213-215,共3页中国科学（信息科学）（英文版）

基　　金：supported by National Key Research Program of China(Grant No.U1936203);Shandong Provincial Natural Science and Foundation(Grant No.ZR2019JQ23);CERNET Innovation Project(Grant No.NGII20190109);Project of Qingdao Postdoctoral Applied Research。

摘　　要：Dear editor,Time series anomaly detection,aiming for identifying unexpected observations within the given time series,has been considered as one of the most challenging studies in time series data mining[1,2].In this study,we present a novel set-based piecewise aggregate representation(SPAR)for anomaly detection,dubbed as SPAR-AD.

关 键 词：PIECEWISE AGGREGATE REPRESENTATION 

分 类 号：O211.61[理学—概率论与数理统计] TP311.13[理学—数学]