文献类型：期刊文献

英文题名：Research on Time Series Anomaly Detection Algorithm Based on Transformer Coupled with GAN

作者：Ye, Mengfei[1]; Wang, Zhanquan[1]; Li, Fei[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2024

起止页码：395

外文期刊名：2024 IEEE 12th International Conference on Information and Communication Networks, ICICN 2024

收录：EI(收录号：20250217670360)

语种：英文

外文关键词：Frequency domain analysis - Generative adversarial networks - Time series

摘要：Timing series anomaly detection plays an important role in several fields. Currently existing methods lack generators with strong generalization ability, and do not sufficiently consider contextual features, time series anomaly detection algorithm based on Transformer coupled with GAN is proposed to solve the problems. The method uses frequency domain preprocessing to extract contextual features, then Transformer-like encoder and decoder architecture is designed by using multi-head self-attention and positional encoding mechanisms to enhance the temporal pattern learning ability, finally uses temporal con-volutional neural network-based discriminator for adversarial training to obtain temporal data anomaly detection results. Experiments on four public datasets show that the algorithm exhibits good performance in time series anomaly detection, especially the F1-score in SMD and MSL reaches 0.974 and 0.968 respectively, which surpass the current state-of-the-art time series anomaly detection algorithm by 1.5%, and the overall experimental results show that the algorithm is reasonable and effective. ? 2024 IEEE.