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Modelling semantics across multiple time series and its applications

文献类型：期刊论文

作者Qiao, Zhi1,3; Huang, Guangyan6; He, Jing4; Zhang, Peng5; Zhang, Yanchun4; Guo, Li2
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2015-09-01
卷号85页码:27-36
关键词Multiple time series Semantics analysis Prediction Anomaly detection
ISSN号0950-7051
DOI10.1016/j.knosys.2015.04.013
英文摘要Analysis based on the holistic multiple time series system has been a practical and crucial topic. In this paper, we mainly study a new problem that how the data is produced underneath the multiple time series system, which means how to model time series data generating and evolving rules (here denoted as semantics). We assume that there exist a set of latent states, which are the system basis and make the system run: data generating and evolving. Thus, there are several challenges on the problem: (1) How to detect the latent states; (2) How to learn the rules based on the states; (3) What the semantics can be used for. Hence, a novel correlation field-based semantics learning method is proposed to learn the semantics. In the method, we first detect latent state assignment by comprehensively considering kinds of multiple time series characteristics, which contain tick-by-tick data, temporal ordering, relationship among multiple time series and so on. Then, the semantics are learnt by Bayesian Markov characteristic. Actually, the learned semantics could be applied into various applications, such as prediction or anomaly detection for further analysis. Thus, we propose two algorithms based on the semantics knowledge, which are applied to make next-n step prediction and detect anomalies respectively. Some experiments on real world data sets were conducted to show the efficiency of our proposed method. (C) 2015 Elsevier B.V. All rights reserved.
资助项目Australia ARC Projects[DE130100911] ; Australia ARC Projects[DP130101327] ; Australia ARC Projects[LP100200682] ; National Science Foundation of China (NSFC)[61332013] ; National Science Foundation of China (NSFC)[61370025] ; National Science Foundation of China (NSFC)[71072172] ; Strategic Leading Science and Technology Projects of CAS[XDA06030200] ; 973 project[2013CB329605] ; Australia ARC Discovery Project[DP140102206]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000359331000002
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/9442]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Qiao, Zhi
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Victoria Univ, Coll Engn & Sci, Ctr Appl Informat, Melbourne, Vic 8001, Australia
5.Univ Technol Sydney, QCIS, Sydney, NSW 2007, Australia
6.Deakin Univ, Sch Informat Technol, Geelong, Vic 3217, Australia
推荐引用方式
GB/T 7714		 Qiao, Zhi,Huang, Guangyan,He, Jing,et al. Modelling semantics across multiple time series and its applications[J]. KNOWLEDGE-BASED SYSTEMS,2015,85:27-36.
APA Qiao, Zhi,Huang, Guangyan,He, Jing,Zhang, Peng,Zhang, Yanchun,&Guo, Li.(2015).Modelling semantics across multiple time series and its applications.KNOWLEDGE-BASED SYSTEMS,85,27-36.
MLA Qiao, Zhi,et al."Modelling semantics across multiple time series and its applications".KNOWLEDGE-BASED SYSTEMS 85(2015):27-36.

入库方式： OAI收割

来源：计算技术研究所

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