Time Series Pattern Recognition

Time Series Pattern Recognition is a systematic methodology within the broader field of pattern recognition that specializes in identifying temporal structures, recurring sequences, and dynamic dependencies in ordered data through time-aware models and feature extraction techniques.

1.1K

Publications

83.7K

Citations

2.9K

Authors

1K

Institutions

Publications per year

2017–2026

446

Authors

2.9K

  1. EK
    Eamonn Keogh

    University of California, Riverside, United States

    48 publications · 10.5K citations

    38

    h-index

  2. JL
    Jessica Lin

    George Mason University, United States

    15 publications · 2.7K citations

    13

    h-index

  3. AB
    Anthony Bagnall

    University of East Anglia, United Kingdom

    14 publications · 2.5K citations

    13

    h-index

  4. AM
    Abdullah Mueen

    University of New Mexico, United States

    12 publications · 1.7K citations

    11

    h-index

  5. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    10 publications · 1.2K citations

    10

    h-index

1–5 of 2.9K

Venues

  1. Data Mining and Knowledge Discovery

    30 publications · 9.5K citations

    28

    h-index

  2. Expert Systems with Applications

    32 publications · 1.5K citations

    24

    h-index

  3. Information Sciences

    28 publications · 2K citations

    21

    h-index

  4. Knowledge-Based Systems

    19 publications · 1.2K citations

    18

    h-index

  5. Applied Soft Computing

    18 publications · 971 citations

    18

    h-index

1–5