Self-supervised multi-transformation learning for time series anomaly detection
-
Published:2024-11
Issue:
Volume:253
Page:124339
-
ISSN:0957-4174
-
Container-title:Expert Systems with Applications
-
language:en
-
Short-container-title:Expert Systems with Applications
Author:
Han HanORCID,
Fan HaoyiORCID,
Huang XunhuaORCID,
Han Chuang
Reference49 articles.
1. A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting;Abbasimehr;Neural Computing and Applications,2022
2. Ahmed, C. M., Palleti, V. R., & Mathur, A. P. (2017). WADI: a water distribution testbed for research in the design of secure cyber physical systems. In Proceedings of the 3rd international workshop on cyber-physical systems for smart water networks (pp. 25–28).
3. Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2020). Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3395–3404).
4. A review paper: noise models in digital image processing;Boyat,2015
5. Neural contextual anomaly detection for time series;Carmona,2021
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献