Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review

Mohamad Issam Sayyaf, Pavel Pascacio, Ni Zhu, Valerie Renaudin

Open source

DOI
10.1109/jsen.2025.3616395
Published
2025-12-15
Container
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1109/jsen.2025.3616395,
  title = {Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review},
  author = {Mohamad Issam Sayyaf and Pavel Pascacio and Ni Zhu and Valerie Renaudin},
  year = {2025},
  journal = {IEEE Sensors Journal},
  doi = {10.1109/jsen.2025.3616395},
  url = {https://doi.org/10.1109/jsen.2025.3616395}
}

RIS

TY  - JOUR
TI  - Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review
AU  - Mohamad Issam Sayyaf
AU  - Pavel Pascacio
AU  - Ni Zhu
AU  - Valerie Renaudin
PY  - 2025
JO  - IEEE Sensors Journal
DO  - 10.1109/jsen.2025.3616395
UR  - https://doi.org/10.1109/jsen.2025.3616395
ER  - 

APA

Sayyaf, M. I., Pascacio, P., Zhu, N., & Renaudin, V. (2025). Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2025.3616395

Source records