TCF-Trans: temporal context fusion transformer for anomaly detection in time series
Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection alg...
Saved in:
Main Authors: | Peng, Xinggan, Li, Hanhui, Lin, Yuxuan, Chen, Yongming, Fan, Peng, Lin, Zhiping |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173799 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach
by: Liu, Yi, et al.
Published: (2022) -
Toward explainable deep anomaly detection
by: PANG, Guansong, et al.
Published: (2021) -
Phase Fourier Reconstruction for Anomaly Detection on Metal Surface Using Salient Irregularity
by: Hung, Tzu-Yi, et al.
Published: (2017) -
Weakly supervised video anomaly detection and localization with spatio-temporal prompts
by: WU, Peng, et al.
Published: (2026) -
Open-vocabulary video anomaly detection
by: WU, Peng, et al.
Published: (2024)