Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking

Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking

Time series anomaly detection is a task that determines whether an unseen signal is normal or abnormal, and it is a crucial function in various real-world applications. Typical approach is to learn normal data representation using generative models, like Generative Adversarial Network (GAN), to disc...

Full description

Saved in:
Bibliographic Details
Main Authors: Ah-Hyung Shin, Seong Tae Kim, Gyeong-Moon Park
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Anomaly detection
masking
self-attention
signal reconstruction
transformer
time series analysis
Online Access:https://ieeexplore.ieee.org/document/10164104/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10164104/

Similar Items