Batch Anomaly Detection

Batch Anomaly Detection is a time series anomaly detection approach that processes data in fixed, non-overlapping segments to identify deviations from normal patterns within each batch, distinguishing itself from streaming or online methods by operating on complete, accumulated datasets rather than continuous, real-time inputs.

36

Publications

2.2K

Citations

116

Authors

69

Institutions

Publications per year

2017–2026

22

Authors

116

  1. CY
    ChangKyoo Yoo

    Kyung Hee University, South Korea

    1 publications · 272 citations

    1

    h-index

  2. YL
    Yue Lu

    University of California, Riverside, United States

    1 publications · 54 citations

    1

    h-index

  3. FS
    Fumin Shen

    University of Electronic Science and Technology of China, China

    1 publications · 54 citations

    1

    h-index

  4. SR
    Salvatore Rampone

    University of Sannio, Italy

    1 publications · 67 citations

    1

    h-index

  5. PA
    Piyush Agarwal

    University of Waterloo, Canada

    1 publications · 48 citations

    1

    h-index

1–5 of 116

Venues

  1. Computers & Chemical Engineering

    5 publications · 374 citations

    5

    h-index

  2. 2

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Information Systems

    1 publications · 28 citations

    1

    h-index

1–5