"Unsupervised anomaly detection in time-series: An extensive evaluation and ..."

Nesryne Mejri et al. (2024)

> Home

Details and statistics

DOI: 10.1016/J.ESWA.2024.124922

access: closed

type: Journal Article

metadata version: 2024-09-08

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/eswa/MejriLRCGA24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/eswa/MejriLRCGA24
Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, Djamila Aouada:
Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods. Expert Syst. Appl. 256: 124922 (2024)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.