Missing Data Robust Anomaly Detection

Missing Data Robust Anomaly Detection is an anomaly detection framework designed to reliably identify anomalous patterns in time series data while maintaining detection accuracy and model stability in the presence of significant proportions of missing data, distinguishing it from standard anomaly detection methods that assume complete or fully imputed data sequences.

20

Publications

1.3K

Citations

74

Authors

50

Institutions

Publications per year

2017–2026

15

Authors

74

  1. JC
    Jinglong Chen

    Xi'an Jiaotong University, China

    2 publications · 60 citations

    2

    h-index

  2. ZG
    Zihui Ge

    AT&T (United States), United States

    2 publications · 370 citations

    2

    h-index

  3. AG
    Albert Greenberg

    AT&T (United States), United States

    2 publications · 370 citations

    2

    h-index

  4. JW
    Jun Wang

    Xi'an Jiaotong University, China

    2 publications · 60 citations

    2

    h-index

  5. ZL
    Zijun Liu

    Xi'an Jiaotong University, China

    2 publications · 60 citations

    2

    h-index

1–5 of 74

Venues

  1. Reliability Engineering & System Safety

    2 publications · 59 citations

    2

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 102 citations

    1

    h-index

  3. IEEE Transactions on Cybernetics

    1 publications · 29 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5