标题:A Novel Multi-resolution Representation for Streaming Time Series
作者:Hu, Yupeng; Jiang, Zifei; Zhan, Peng; Zhang, Qingke; Ding, Yiming; Li, Xueqing
通讯作者:Li, XQ;Li, Xueqing
作者机构:[Hu, Yupeng; Jiang, Zifei; Zhan, Peng; Ding, Yiming; Li, Xueqing] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.; [ 更多
会议名称:6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)
会议日期:OCT 19-21, 2017
来源:2017 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
出版年:2018
卷:129
页码:178-184
DOI:10.1016/j.procs.2018.03.069
关键词:Internet of things; Streaming time series; Online segmentation;; Multi-resolution representation
摘要:Along with the coming of IoT (Internet of things) era, massive numbers of instruments and applications in various fields are continuously producing oceans of time series stream data, which could be characterized by its large amount, high dimensionality and continuity nature. In order to carry out different kinds of data mining tasks (similarity search, classification, clustering, prediction etc.) based on streaming time series efficiently and effectively, segmentation and representation which segment a streaming time series into several subsequences and provide more compact representation for the raw data, should be done as the first step. With the virtue of solid theoretical foundations, piecewise linear representation (PLR) has been gained success in yielding more compact representation and fewer segments, however, the current state of art PLR methods have their own flaws. In this paper, we propose a novel online time series segmentation algorithm called continuous segmentation and multi-resolution representation algorithm based on turning points (CSMR_TP), which partitions the streaming time series by a set of temporal feature points and represents the time series flexibly. Our method can not only generate more accurate approximation than the state-of-the-art of PLR algorithm, but also represent the streaming time series in a more flexible way to meet the diverse needs of users. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method. Copyright (C) 2018 Elsevier Ltd. All rights reserved.
收录类别:CPCI-S;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-s2.0-85047084020&doi=10.1016%2fj.procs.2018.03.069&partnerID=40&md5=066152aa315999e0629a44f19c8d1c22
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