Multivariate time series anomaly detection via dynamic graph attention network and Informer-Reference-Cited by-同舟云学术

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Multivariate time series anomaly detection via dynamic graph attention network and Informer

Published:2024-06-11 Issue:17-18 Volume:54 Page:7636-7658
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Huang Xiangheng,Chen Ningjiang,Deng Ziyue,Huang Suqun

Funder

National Natural Science Foundation of China

Guangxi University Natural Science and Technology innovation and development doubling plan project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05575-y.pdf

Reference25 articles.

1. Xia K, Sacco C, Kirkpatrick M, Saidy C, Nguyen L, Kircaliali A, Harik R (2021) A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J Manuf Syst 58:210–230

2. Hu P, He C, Zhu Y, Li T (2023) The product quality inspection scheme based on software-defined edge intelligent controller in industrial internet of things. J Cloud Comput 12(1):113

3. Zhao H, Wang Y, Duan J, Huang C, Cao D, Tong Y, Xu B, Bai J, Tong J, Zhang Q (2020) Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International conference on data mining (ICDM), pp 841–850. IEEE

4. Zhan J, Wang S, Ma X, Wu C, Yang C, Zeng D, Wang S (2022) Stgat-mad: Spatial-temporal graph attention network for multivariate time series anomaly detection. In: ICASSP 2022-2022 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 3568–3572. IEEE

5. Chen Z, Chen D, Zhang X, Yuan Z, Cheng X (2021) Learning graph structures with transformer for multivariate time-series anomaly detection in iot. IEEE Internet Things J 9(12):9179–9189

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