Transformer-based Anomaly Detection

Transformer-Based Anomaly Detection is a method for identifying anomalous patterns in time series data by leveraging the self-attention mechanism of transformer architectures to model long-range temporal dependencies more effectively than traditional sequential models.

157

Publications

12.9K

Citations

644

Authors

251

Institutions

Publications per year

2017–2026

152

Authors

644

  1. CP
    Claudio Piciarelli

    University of Udine, Italy

    3 publications · 402 citations

    3

    h-index

  2. PK
    Pankaj Kumar Mishra

    University of Udine, Italy

    3 publications · 402 citations

    3

    h-index

  3. WU
    Waseem Ullah

    Sejong University, South Korea

    3 publications · 194 citations

    3

    h-index

  4. SW
    Sung Wook Baik

    Sejong University, South Korea

    3 publications · 194 citations

    3

    h-index

  5. CL
    Chengliang Liu

    Harbin Institute of Technology, China

    3 publications · 148 citations

    3

    h-index

1–5 of 644

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 2.2K citations

    11

    h-index

  2. IEEE Access

    10 publications · 648 citations

    9

    h-index

  3. 5

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 318 citations

    3

    h-index

  5. Sensors

    3 publications · 75 citations

    3

    h-index

1–5