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融合趋势信息的时间序列符号聚合近似方法 被引量:3

Time series symbolic aggregate approximation method for fusion trend information
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摘要 为解决符号聚合近似方法(SAX)表示时间序列时忽略序列局部趋势的问题,提出一种融合形态趋势信息的时间序列符号聚合近似方法。该方法以子序列段中的最大值和最小值以及它们两者之间的相对位置为依据,定义一种新的趋势指标来描述子序列段的趋势,并使用融合趋势指标的符号矢量来近似表示时间序列。针对所提表示方法,给出一种新的距离度量方法,并在UCR数据集和电机转矩数据集上进行分类实验,实验结果表明,所提方法在绝大多数数据集上获得了较SAX方法更高的分类准确率,能够有效弥补SAX方法表示时间序列时忽略局部趋势的不足。 To solve the problem of losing trend information when representing time series with symbolic aggregate approximation method(SAX),this paper proposed a new time series symbolic aggregate approximation method integrating morphological trend information.Based on the maximum and minimum values in the subsequence and their relative positions,this method defined a new trend index to describe the trend information of the subsequence segments,and used the symbol vector integrating trend index to approximately represent the time series.For the proposed representation method,this paper gave a new distance metric and used it to conduct classification experiments on the UCR datasets and motor torque dataset.The experimental results show that the proposed method obtains higher classification accuracy than the SAX method on most datasets,and can effectively make up for the deficiency of losing local trend when the SAX method represents time series.
作者 黄俊杰 徐兴华 崔小鹏 康军 杨皓翔 Huang Junjie;Xu Xinghua;Cui Xiaopeng;Kang Jun;Yang Haoxiang(National Key Laboratory of Science&Technology on Vessel Integrated Power System,Naval University of Engineering,Wuhan 430033,China)
出处 《计算机应用研究》 CSCD 北大核心 2023年第1期86-90,共5页 Application Research of Computers
基金 国家自然科学基金项目 湖北省自然科学基金项目。
关键词 时间序列 符号化表示 符号化聚合近似 趋势信息 time series symbolic representation symbolic aggregate approximation trend information
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  • 1肇刚,李言俊.基于时间序列数据挖掘的航天器故障诊断方法[J].飞行器测控学报,2010,29(3):1-5. 被引量:10
  • 2肖辉,胡运发.基于分段时间弯曲距离的时间序列挖掘[J].计算机研究与发展,2005,42(1):72-78. 被引量:59
  • 3张建业,潘泉,张鹏,梁建海.基于斜率表示的时间序列相似性度量方法[J].模式识别与人工智能,2007,20(2):271-274. 被引量:36
  • 4Daw C S, Finney C E A, Tracy E R. A review of symbolic analysis of experimental data. Review of Scientific Instruments, 2003, 74(2): 915-930 被引量:1
  • 5Kantz H, Schreiber T. Nonlinear Time Series Analysis. 2nd Edition. Cambridge, UK: Cambridge University Press, 2004 被引量:1
  • 6Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time-series databases//Proceedings of the ACM SIGMOD International Conference on Management of Data. Minneapolis, MN, 1994: 419-429 被引量:1
  • 7Chan K, Fu A W. Efficient time series matching by wavelets//Proceedings of the 15th IEEE International Conference on Data Engineering. Sydney, Australia, 1999:126-133 被引量:1
  • 8Keogh E, Chakrabarti K, Pazzani M, Mehrotra S. Locally adaptive dimensionality reduction for indexing large time series databases//Proeeedings of the ACM SIGMOD Conference on Management of Data. Santa Barbara, CA, 2001: 151-162 被引量:1
  • 9Geurts P. Pattern extraction for time series classification// Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery. Freiburg, Germany, 2001:115-127 被引量:1
  • 10Keznel M B, Buhl M. Estimating good discrete partitions form observed data: Symbolic false nearest neighbors. Physical Review E, 2003, 91(8): 084102 被引量:1

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