标题:Multi-resolution representation with recurrent neural networks application for streaming time series in IoT
作者:Hu, Yupeng; Ren, Pengjie; Luo, Wei; Zhan, Peng; Li, Xueqing
作者机构:[Hu, Yupeng; Luo, Wei; Zhan, Peng; Li, Xueqing] Shandong Univ, Sch Software, Jinan, Shandong, Peoples R China.; [Ren, Pengjie] Univ Amsterdam, Inst 更多
通讯作者:Li, Xueqing;Li, XQ
通讯作者地址:[Li, XQ]Shandong Univ, Sch Software, Jinan, Shandong, Peoples R China.
来源:COMPUTER NETWORKS
出版年:2019
卷:152
页码:114-132
DOI:10.1016/j.comnet.2019.01.035
关键词:Internet of things; Streaming time series; Multi-resolution; representation; Time series classification; Recurrent neural networks
摘要:Nowadays, with the proliferation of IoT (Internet of Things), we have gradually entered into a new IoE (Internet of Everything) era, in which billions of connected devices in widespread fields are constantly producing oceans of streaming time series. In order to conduct in-depth data mining researches (similarity searching, classification, clustering, prediction, etc.) based on streaming time series efficiently and effectively, time series representation should be done as the first step. In this paper, we propose a novel multi-resolution hybrid representation approach for streaming time series, which can not only generate different types of representation results in a more flexible way to meet diverse needs of users, but also be utilized as a useful preprocessing tool for the subsequent time series data mining researches. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method. (C) 2019 Elsevier B.V. All rights reserved.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061339914&doi=10.1016%2fj.comnet.2019.01.035&partnerID=40&md5=d4c9e71213ccda3a394517e7a3128863
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