Autoencoder-based Anomaly Detection

Autoencoder-Based Anomaly Detection is a method for identifying anomalous patterns in time series data by leveraging an autoencoder neural network to model normal behavior and detecting anomalies through reconstruction error deviation.

1K

Publications

69.6K

Citations

3.7K

Authors

1.1K

Institutions

Publications per year

2017–2026

1K

Authors

3.7K

  1. YL
    Yunsong Li

    Xidian University, China

    8 publications · 616 citations

    8

    h-index

  2. WX
    Weiying Xie

    Xidian University, China

    7 publications · 570 citations

    7

    h-index

  3. MP
    M. Pierini

    European Organization for Nuclear Research, Switzerland

    6 publications · 284 citations

    6

    h-index

  4. JC
    Jinglong Chen

    Xi'an Jiaotong University, China

    6 publications · 286 citations

    6

    h-index

  5. MS
    Mohammad Sabokrou

    Institute for Research in Fundamental Sciences, Iran

    6 publications · 867 citations

    6

    h-index

1–5 of 3.7K

Venues

  1. IEEE Access

    49 publications · 3.1K citations

    31

    h-index

  2. arXiv (Cornell University)

    Repository · 53 publications · 2.4K citations

    26

    h-index

  3. Sensors

    35 publications · 1.6K citations

    24

    h-index

  4. 18

    h-index

  5. Applied Sciences

    16 publications · 506 citations

    15

    h-index

1–5