Dual Transformers With Latent Amplification for Multivariate Time Series Anomaly Detection
Anomaly detection in multivariate time series is crucial for applications such as industrial monitoring, cybersecurity, and healthcare. Transformer-based reconstruction methods have recently shown strong performance but often suffer from overgeneralization, where anomalies are reconstructed too accu...
Saved in:
| Main Authors: | Yeji Choi, Kwanghoon Sohn, Ig-Jae Kim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11105392/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Anomaly detection model for multivariate time series based on stochastic Transformer
by: Weigang HUO, et al.
Published: (2023-02-01) -
An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions
by: Shijie Tang, et al.
Published: (2025-07-01) -
Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU
by: Yajing Xing, et al.
Published: (2025-05-01) -
A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions
by: Fengling Wang, et al.
Published: (2025-01-01) -
An Anomaly Detection Method for Multivariate Time Series Data Based on Variational Autoencoders and Association Discrepancy
by: Haodong Wang, et al.
Published: (2025-04-01)