Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data

  • Authors:
  • S. S. Liao;T. H. Tang;Wei-Yi Liu

  • Affiliations:
  • Dept. of Inf. Syst., City Univ. of Hong Kong, Yunnan, China;-;-

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2004

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Abstract

Finding similar sequences in time series has received much attention and is a widely studied topic. Most existing approaches in the time series area focus on the efficiency of algorithms but seldom provide a means to handle imprecise data. In this paper, a more general approach is proposed to measure the distance of time sequences containing crisp values, intervals, and fuzzy intervals as well. The concept of distance measurement and its associated dynamic-programming-based algorithms are described. In addition to finding the sequences with similar evolving trends, a means of finding the sequences with opposite evolving tendencies is also proposed, which is usually omitted in current related research but could be of great interest to many users.