期刊文献+

基于趋势转折点的时间序列模式表示 被引量:2

Representation of Time Series Based on Trend Turning Points
下载PDF
导出
摘要 在时间序列分段线性表示基础上,提出一种新的基于趋势转折点的时间序列模式表示方法。该方法在充分利用时间序列时变特征的基础上,可以有效地提取时间序列中的趋势及压缩原始数据。仿真实验证明,该方法具有高效、实现简便、效果直观的优点,对于油田测井领域的数据适应性良好。 Based on PLR (piecewise linear representation) of time series,a new representation method of time-series based on trend turning point is proposed.The method takes full advantage of time-varying characteristics of time-series,and can effectively extract the trend of the time series and compress the primary data.The simulation results show that the method is efficient,simple and intuitive,and the method has good adaptability to oil well logging data.
作者 肖红 尚福华
出处 《科学技术与工程》 2010年第13期3254-3257,共4页 Science Technology and Engineering
基金 中国博士后科学基金(20080440923) 黑龙江省自然科学基金(F2007-11) 黑龙江省教育厅科研课题(11521005)资助
关键词 时间序列 分段线性表示 趋势转折点 time series PLR trend turning point
  • 相关文献

参考文献8

二级参考文献48

  • 1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
  • 2Jia-WeiHan,JianPei,Xi-FengYan.From Sequential Pattern Mining to Structured Pattern Mining: A Pattern-Growth Approach[J].Journal of Computer Science & Technology,2004,19(3):257-279. 被引量:18
  • 3周洪宝,闵珍.基于时间序列的城市用水量预测问题的研究[J].微计算机信息,2006,22(10X):82-84. 被引量:6
  • 4杜奕,卢德唐,李道伦,赵亦朋.一种快速的时间序列线性拟合算法[J].中国科学技术大学学报,2007,37(3):310-314. 被引量:16
  • 5Agrawal R,Faloutsos C,Swami A.Efficient similarity search in sequence databases[C]//Proc of the Fourth Int'l Conference on Foundations of Data Organization and Algorithms.London:Springer Verlag, 1993 : 69-84.
  • 6Chan K P,FU W C.Efficient time series matching by wavelets[C]// Proceedings of the International Conference on Data Engineering. Washington: IEEE Computer Society, 1999 : 126-133.
  • 7Korn F,Jagadish H V,Faloutsos C.Efficiently supporting Ad hoc queries in large datasets of time sequences[C]//Proceedings of the ACM SIGMOD Conference on Management of Data.New York: ACM Press, 1997:289-300.
  • 8Park S,Chu W W,Yoon J,et al.Similarity search of time-warped subsequences via a suffix tree[J].Information Systems,2003,28(7): 867-883.
  • 9Prat K B,Fink E.Search for patterns in compressed time series [J]. International Journal of hnage and Graphics, 2002,2( 1 ) : 89-106.
  • 10Keogh E J, Chakrabarti K,Pazzani M J,et al.Dimensionality reduction for fast similarity search in large time series databases [J]. Knowl Inf Syst,2001,3(3):263-286.

共引文献91

同被引文献20

  • 1李爱国,覃征.在线分割时间序列数据[J].软件学报,2004,15(11):1671-1679. 被引量:27
  • 2张军,吴绍春,王炜.多变量时间序列模式挖掘的研究[J].计算机工程与设计,2006,27(18):3364-3366. 被引量:11
  • 3LIN J,KEOGH E,LONARDI S,et al.A symbolic repre sentation of time series with implications for streaming algorithms[C].Proceedings of the 8th ACM SIGMOD Work-shop on Research Issues in Data Mining and Knowledge Discovery,2003:2-11.
  • 4GULLO F,PONTI G,RAGARELLI A,et al.A time series representation model for accurate and fast similarity detection[J].Pattern Recognition,2009,42(11):2998-3014.
  • 5AGRAWAL R,FALOUTSOS C,WAMI A S.Efficient similarity search in sequence databases[C].Proc.of 4th International Conference on Foundations of Data Organization and Algorithms,London:Springer-Verlag,1993:69-84.
  • 6KEOGH E.Data mining and machine learning in time series database[C].Proc.of 5th Industrial Conference on Data Mining,2005.
  • 7KEOGH E,PAZZANIM C.Dimensionality reduction for fast similarity search in large time series databases[J].Journal of Knowledge and Information Systems,2001,3(3):263-286.
  • 8贾澎涛,林卫,何华灿.时间序列的自适应误差约束分段线性表示[J].计算机工程与应用,2008,44(5):10-13. 被引量:9
  • 9尚福华,孙达辰.基于时间序列趋势转折点的分段线性表示[J].计算机应用研究,2010,27(6):2075-2077. 被引量:21
  • 10王选宏,肖云.基于核模糊C均值的异常检测方法[J].科学技术与工程,2010,10(23):5793-5795. 被引量:2

引证文献2

二级引证文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部