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Related Experiment Videos

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

Stephen Shaoyi Liao1, Tony Heng Tang, Wei-Yi Liu

  • 1Department of Information Systems, City University of Hong Kong, Hong Kong. issliao@cityu.edu.hk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 27, 2004
PubMed
Summary

This study introduces a novel method for time series analysis, enabling the measurement of distances between sequences with imprecise data, including fuzzy intervals. It also offers a way to identify sequences with opposite trends, enhancing time series similarity search.

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Area of Science:

  • Data Science
  • Computer Science
  • Statistics

Background:

  • Time series similarity search is a crucial task in data analysis.
  • Existing methods often prioritize algorithmic efficiency over handling imprecise data.
  • Imprecise data, such as intervals and fuzzy intervals, are common in real-world time series.

Purpose of the Study:

  • To propose a generalized approach for measuring distances between time series, accommodating crisp values, intervals, and fuzzy intervals.
  • To develop dynamic programming-based algorithms for this generalized distance measurement.
  • To enable the identification of time series with both similar and opposite evolving trends.

Main Methods:

  • A novel distance measurement concept is introduced for time series data.

Related Experiment Videos

  • Dynamic programming algorithms are employed to efficiently compute these distances.
  • The approach is extended to detect sequences with opposite evolving tendencies.

    • Main Results:

    • The proposed method effectively handles time series containing crisp values, intervals, and fuzzy intervals.
    • The dynamic programming algorithms provide an efficient means for distance calculation.
    • The capability to find sequences with opposite trends is demonstrated.

    Conclusions:

    • The developed approach offers a more comprehensive solution for time series similarity search by incorporating imprecise data.
    • The inclusion of opposite trend detection expands the utility of time series analysis for various applications.
    • This research contributes to more robust and versatile time series data mining techniques.