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一种变步长趋势子序列搜索算法 被引量:3

Variable step algorithm for sub-trend sequence searching
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摘要 为了克服基于点距离的时间序列相似性搜索物理概念模糊和速度慢的缺点,提出时间序列的分段趋势序列(PTS)概念,并在此基础上提出一种变步长趋势子序列搜索算法.该算法基于时间序列分段线性表示理论,通过相似阈值和子序列间的趋势距离计算跳跃步长,从跳跃步长后开始的子序列进行下一次匹配,从而对全序列实现跳跃式搜索。理论分析和仿真结果表明,该算法对基于趋势表示的子序列搜索在时间和空间上都具有更优的性能,适用于时间序列的动态特征分析. To overcome the shortcomings of concept indistinct and slow speed in time series similarity searching based on point distance, a piecewise trend sequence (PTS) and a variable step algorithm for sub-trend sequence searching based on PTS were proposed. The algorithm was founded on the theory of piecewise linear representation and calculated skip steps with similarity threshold and trend distance between sub-series. The next matching started after skip steps and skip-searching for whole series was realized. Theoretic analysis and simulation indicate that the algorithm has better performance for sub-trend searching in temporal and space, and is useful in time series dynamic feature analysis.
出处 《浙江大学学报(工学版)》 EI CAS CSCD 北大核心 2004年第12期1566-1569,共4页 Journal of Zhejiang University:Engineering Science
基金 国家"863"高技术发展计划资助项目(2001AA411210 2001AA413220) 国家"973"重点基础研究发展规划资助项目(2002CB31220304).
关键词 趋势序列 子序列搜索 数据挖掘 Computer simulation Learning algorithms Numerical analysis Time series analysis
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参考文献8

  • 1AGRAWAL R, FALOUTSOS C, SWAMI A. Efficient similarity search in sequence database [A]. Proceedings of 4th International Conference on Foundations of Data Organization and Algorithm [C]. New York: Springer, 1993: 69-84.
  • 2CHAN K, FU W. Efficient time series matching by wavelets [A]. Proceedings of the 15th IEEE Inter national Conference on Data Engineering [C]. Sydney: IEEE, 1999: 126-133.
  • 3YOON J, LEE J, KIM S. Trend similarity and prediction in time-series databases [A]. Proceedings of SPIE on Data Mining and Knowledge Discovery: Theory, Tools, and Technology II [C]. Washington: SPIE,2000: 201-212.
  • 4KEOGH E. Fast similarity search in the presence of longitudinal scaling in time series databases [A]. Proceedings of the 9th International Conference on Tools with Artificial Intelligence [C]. Newport Beach: IEEE, 1997: 578-584.
  • 5BAEZA-Yates R, NAVARRO G. A faster algorithm for approximate string matching [A]. Proceedings of CPM'96 [C]. New York: Spirnger,1996: 1-23.
  • 6BAEZA-YATES R, NAVARRO G. A practical index for text retrieval allowing errors [J]. CLEI, 1997, 1: 273-282.
  • 7郭斯羽,吴铁军.一种挖掘相似子趋势的可变递增步长算法[J].浙江大学学报(工学版),2002,36(4):421-426. 被引量:1
  • 8KEOGH E, KASETTY S. On the need for time series data mining benchmarks: A survey and empirical demonstration [A]. Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [C]. Edmonton: ACM, 2002: 102-111.

二级参考文献7

  • 1[1]AGRAWAL R, FALOUTSOS C, SWAMI A. Efficient similarity search in sequence databases [A]. Proc of 4th Int'l Conf on Foundation of Data Organization and Algorithms [C]. New York: Springer, 1993. 69-84.
  • 2[2]AGRAWAL R, LIN K, SAWHNEY H, et al. Fast similarity search in the presence of noise, scaling, and translation in time-Series databases [A]. Proc of the Very Large Data Bases Conf [C]. San Francisco: Morgan Kaufmann, 1995. 490-501.
  • 3[3]YOON J P, LEE J, KIM S. Trend similarity and prediction in time-series databases [A]. Proc of SPIE on Data Mining and Knowledge Discovery: Theory, Tools, and Technology II [C]. Washington: SPIE, 2000. 201-212.
  • 4[4]BAEZA-YATES R A, PERLEBERG C H. Fast and practical approximate pattern matching [A]. Proc of Combinatorial Pattern Matching, Third Annual Symposium [C]. New York: Springer, 1992. 185-192.
  • 5[5]CALIFANO A, RIGOUTSOS I. FLASH: A fast look-up algorithm for string homology [A]. Proc of the 1st Intl, Conf on Intelligent Systems for Molecular Biology [C]. Menlo Park: AAAI Press, 1993. 56-64.
  • 6[6]BAEZA-YATES R A, NAVARRO G. A faster algorithm for approximate string matching [A]. Proc of Combinatorial Pattern Matching, 7th Annual Symposium [C]. New York: Springer, 1996. 1-23.
  • 7[7]BAEZA-YATES R, NAVARRO G. A practical index for text retrieval allowing errors [J]. CLEI, 1997, 1: 273-282.

同被引文献27

  • 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
  • 2Agrawal R,Faloutsos C,Swami A.Efficient similarity search in sequence databases[C].In:Proceedings of the 4th Conference on Foundations of Data Organization and Algorithms,1993:69~84
  • 3Rafiei D,Mendelzon A.Efficient retrieval of similar time sequences using DFT[C].In:Proceedings of the 5th International Conference on Foundations of Data Organizations and Algorithms,1998:249~257
  • 4Das G,Lin K,Mannila H et al.Rule discovery from time series[C].In:Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining,1998:16~22
  • 5Chan K,Fu W.Efficient time series matching by wavelets[C].In:Proceedings of the 15th IEEE International Conference on Data Engineering,1999:126~133
  • 6Beckmann N,Kriegel H-P,Schneider R et al.The R*-tree:An efficient and robust access method for points and rectangles[C].In:Proceedings of ACM SIFMOD International Conference on Management of Data,1990:322~331
  • 7Keogh E,Pazzani M.An enhanced representation of time series which allows fast and accurate classification,clustering and relevance feedback[C].In:Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining,1998:239~241
  • 8Keogh E,Smyth P.A probabilistic approach to fast pattern matching in time series databases[C].In:Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining,1997:24~20
  • 9Keogh E,Pazzani Michael J.An indexing scheme for fast similarity search in large time series databases[C].In:Proceedings of the 11th International Conference on Scientific and Statistical Database Management,1999:56~67
  • 10Keogh E,Chakrabarti K,Pazzani M et al.Dimensionality reduction for fast similarity search in large time series databases[J].Knowledge and Information Systems,2001; 3 (3):263~286

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