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

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

作者机构：School of Software Shandong University Informatization Office Shandong University School of Computer Science and Technology Shandong University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2021年第64卷第4期

页      面：217-219页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

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 

主　　题：Time Series Multi-domain Representation Anomaly Detection Data Mining 

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.