Modelling semantics across multiple time series and its applications

doi:10.1016/j.knosys.2015.04.013

	Modelling semantics across multiple time series and its applications
	Qiao, Zhi 1,3; Huang, Guangyan 6; He, Jing 4; Zhang, Peng 5; Zhang, Yanchun 4; Guo, Li 2
	2015-09-01
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
卷号	85 页码:27-36
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.
关键词	Multiple time series Semantics analysis Prediction Anomaly detection
DOI	10.1016/j.knosys.2015.04.013
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000359331000002
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9443
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.