Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and …
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both …
Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention …
The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, eg, multi-sensor systems represent a critical enabling …
W Wu, C Song, J Zhao, Z Xu - Information Sciences, 2023 - Elsevier
Industrial cyber-physical systems (ICPSs) play an important role in many critical infrastructures. To ensure the secure and reliable operation of ICPSs, this work presents a …
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches …
A Huet, JM Navarro, D Rossi - Proceedings of the 28th ACM SIGKDD …, 2022 - dl.acm.org
In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall …
H Xu, Y Wang, S Jian, Q Liao, Y Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality …
CYA Lai, FK Sun, Z Gao, JH Lang… - Advances in Neural …, 2024 - proceedings.neurips.cc
Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to …