标题:Feature-based online segmentation algorithm for streaming time series (Short Paper)
作者:Zhan, Peng ;Hu, Yupeng ;Luo, Wei ;Xu, Yang ;Zhang, Qi ;Li, Xueqing
通讯作者:Hu, Yupeng
作者机构:[Zhan, P] School of Software, Shandong University, Jinan, Shandong, China;[ Hu, Y] School of Software, Shandong University, Jinan, Shandong, China;[ L 更多
会议名称:14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018
会议日期:1 December 2018 through 3 December 2018
来源:Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
出版年:2019
卷:268
页码:477-487
DOI:10.1007/978-3-030-12981-1_33
关键词:Algorithm; Data mining; Online segmentation; Streaming time series
摘要:Over the last decade, huge number of time series stream data are continuously being produced in diverse fields, including finance, signal processing, industry, astronomy and so on. Since time series data has high-dimensional, real-valued, continuous and other related properties, it is of great importance to do dimensionality reduction as a preliminary step. In this paper, we propose a novel online segmentation algorithm based on the importance of TPs to represent the time series into some continuous subsequences and maintain the corresponding local temporal features of the raw time series data. To demonstrate the advantage of our proposed algorithm, we provide extensive experimental results on different kinds of time series datasets for validating our algorithm and comparing it with other baseline methods of online segmentation. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
收录类别:EI;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-s2.0-85062298684&doi=10.1007%2f978-3-030-12981-1_33&partnerID=40&md5=f51034eab88dad9c358d0da7852f3c17
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