标题:A Continuous Segmentation Algorithm for Streaming Time Series
作者:Hu, Yupeng; Ji, Cun; Jing, Ming; Ding, Yiming; Kuai, Shuo; Li, Xueqing
通讯作者:Li, XQ
作者机构:[Hu, Yupeng; Ji, Cun; Jing, Ming; Ding, Yiming; Kuai, Shuo; Li, Xueqing] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.; [ 更多
会议名称:12th International Conference on Collaborate Computing - Networking, Applications and Worksharing (CollaborateCom)
会议日期:NOV 10-11, 2016
来源:COLLABORATE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING, COLLABORATECOM 2016
出版年:2017
卷:201
页码:140-151
DOI:10.1007/978-3-319-59288-6_13
关键词:Data mining; Time series; Online segmentation; Algorithms
摘要:Along with the arrival of Industry 4.0 era, massive numbers of detecting instruments in various fields are continuously producing a plenty number of time series stream data. In order to efficiently and effectively analyze and mine the high-dimensional streaming time series, the segmentation which provides more accurate representation to the raw time series data, should be done as the first step. In this paper, we propose a novel online segmentation approach based on the turning points to partition the time series into some continuous subsequences and maintain a high similarity between the processed subsequences and the raw data. It achieves the best overall performance on the segmentation results compared with other baseline methods. Extensive experiments on all kinds of typical time series datasets have been conducted to demonstrate the advantages of our method.
收录类别:CPCI-S;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021966086&doi=10.1007%2f978-3-319-59288-6_13&partnerID=40&md5=f9c4334a5e8eb4980e498b069ea514cd
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