Streaming Anomaly Detection

Streaming Anomaly Detection is an anomaly detection framework designed for time series data that operates under streaming conditions, distinguishing itself by processing data points sequentially in real-time with limited memory and computational resources, while continuously identifying anomalies without prior knowledge of their occurrence.

481

Publications

24.5K

Citations

1.5K

Authors

642

Institutions

Publications per year

2017–2026

278

Authors

1.5K

  1. BH
    Bryan Hooi

    National University of Singapore, Singapore

    7 publications · 369 citations

    7

    h-index

  2. CL
    Christopher Leckie

    The University of Melbourne, Australia

    7 publications · 222 citations

    7

    h-index

  3. BT
    Bhavani Thuraisingham

    The University of Texas at Dallas, United States

    7 publications · 298 citations

    7

    h-index

  4. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    7 publications · 704 citations

    7

    h-index

  5. SC
    Saihua Cai

    Jiangsu University, China

    6 publications · 101 citations

    6

    h-index

1–5 of 1.5K

Venues

  1. Expert Systems with Applications

    16 publications · 571 citations

    15

    h-index

  2. Information Sciences

    15 publications · 470 citations

    13

    h-index

  3. arXiv (Cornell University)

    Repository · 14 publications · 450 citations

    12

    h-index

  4. Knowledge-Based Systems

    9 publications · 407 citations

    9

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 8 publications · 405 citations

    8

    h-index

1–5