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

Stephen Shaoyi Liao·Tony Heng Tang·Wei-Yi Liu
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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.

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AlgorithmsArtificial IntelligenceComputer SimulationFuzzy LogicInformation Storage and RetrievalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedSignal Processing, Computer-AssistedEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Stephen Shaoyi Liao, Tony Heng Tang, Wei-Yi Liu. 2004. Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data.. https://doi.org/10.1109/tsmcb.2004.833597

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