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基于时间序列波动性的分段线性表示方法

李颖^1,2, 于东^1,3, 胡毅^1,3, 刘劲松^1,2, 张丽鹏^1,2

(1.中国科学院沈阳计算技术研究所, 沈阳 110168;2.中国科学院大学, 北京 100049;3.沈阳中科数控技术股份有限公司, 沈阳 110168)

Piecewise Linear Representation Based on Time Series Volatility

LI Ying^1,2, YU Dong^1,3, HU Yi^1,3, LIU Jin-Song^1,2, ZHANG Li-Peng^1,2

(1.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Shenyang CASNC Technology Co. Ltd., Shenyang 110168, China)

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本文已被：浏览 771次下载 1388次
Received:October 12, 2020 Revised:November 05, 2020

中文摘要: 为了解决现有时间序列的分段线性表示方法忽略时间序列的全局特征, 局限于局部最优的问题, 本文通过研究时间序列的趋势, 发现了时间序列的波动特性, 将时间序列的趋势变化分为上下两层, 在上下两层分别剔除趋势保持点. 实验结果表明, 该分段方法时间复杂度低、且易于实现, 在保持时间序列趋势特征的基础上, 得到的拟合误差更小.

中文关键词: 时间序列分段波动全局趋势关键点

Abstract:Existing piecewise linear representation of time series ignores the global characteristics of time series and easily falls into local optima. To solve this, the paper studies the trend in time series and finds its fluctuation. The trends is divided into an upper layer and a lower one with their trend holding points removed. The experimental results show that the segmentation method has low time complexity and is easy to implement, and the fitting error is smaller on the premise of keeping the trend characteristics of time series.

keywords: time series subsection wave global trend key points

文章编号： 中图分类号： 文献标志码：

基金项目:国家科技重大专项(2019ZX04004-001)

Author Name	Affiliation	E-mail
LI Ying	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China	liying185@mails.ucas.ac.cn
YU Dong	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China Shenyang CASNC Technology Co. Ltd., Shenyang 110168, China
HU Yi	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China Shenyang CASNC Technology Co. Ltd., Shenyang 110168, China
LIU Jin-Song	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China
ZHANG Li-Peng	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China

Author Name	Affiliation	E-mail
LI Ying	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China	liying185@mails.ucas.ac.cn
YU Dong	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China Shenyang CASNC Technology Co. Ltd., Shenyang 110168, China
HU Yi	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China Shenyang CASNC Technology Co. Ltd., Shenyang 110168, China
LIU Jin-Song	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China
ZHANG Li-Peng	Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China University of Chinese Academy of Sciences, Beijing 100049, China

引用文本：
李颖,于东,胡毅,刘劲松,张丽鹏.基于时间序列波动性的分段线性表示方法.计算机系统应用,2021,30(6):300-305
LI Ying,YU Dong,HU Yi,LIU Jin-Song,ZHANG Li-Peng.Piecewise Linear Representation Based on Time Series Volatility.COMPUTER SYSTEMS APPLICATIONS,2021,30(6):300-305