Subsequence Anomaly Detection

Subsequence Anomaly Detection is a method within the field of time series anomaly detection that identifies anomalous patterns by analyzing variable-length segments of the time series, distinguishing itself from point-based or window-level approaches through its focus on detecting irregular temporal subsequences that deviate significantly from normal behavioral motifs.

68

Publications

5.1K

Citations

191

Authors

81

Institutions

Publications per year

2017–2026

41

Authors

191

  1. EK
    Eamonn Keogh

    University of California, Riverside, United States

    8 publications · 1.4K citations

    9

    h-index

  2. TP
    Themis Palpanas

    Université Paris Cité, France

    6 publications · 316 citations

    6

    h-index

  3. PB
    Paul Boniol

    Université Paris Cité, France

    6 publications · 316 citations

    6

    h-index

  4. JL
    Jessica Lin

    George Mason University, United States

    3 publications · 996 citations

    3

    h-index

  5. WP
    Witold Pedrycz

    University of Alberta, Canada

    3 publications · 201 citations

    3

    h-index

1–5 of 191

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 749 citations

    7

    h-index

  2. IEEE Access

    6 publications · 188 citations

    6

    h-index

  3. Expert Systems with Applications

    2 publications · 64 citations

    2

    h-index

  4. Applied Sciences

    2 publications · 44 citations

    2

    h-index

  5. Knowledge and Information Systems

    2 publications · 315 citations

    2

    h-index

1–5