GenAD: General unsupervised anomaly detection using multivariate time series for large‐scale wireless base stations-Reference-Cited by-同舟云学术

GenAD: General unsupervised anomaly detection using multivariate time series for large‐scale wireless base stations

Published:2022-12-11 Issue:1 Volume:59 Page:
ISSN:0013-5194
Container-title:Electronics Letters
language:en
Short-container-title:Electronics Letters

Author:

Hua Xiaolei¹,Zhu Lin¹,Zhang Shenglin²,Li Zeyan³,Wang Su⁴,Deng Chao¹,Feng Junlan¹,Zhang Zhao⁴,Wu Wei⁵

Affiliation:

1. JIUTIAN TeamChina Mobile Research InstituteBeijingChina

2. College of SoftwareNankai UniversityTianjinChina

3. Dept. of Computer Sciense and TechnologyTsinghua UniversityBeijingChina

4. School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

5. Dept. of NetworkChina Mobile Group Guangdong Company LimitedGuangdongChina

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ell2.12683

Reference8 articles.

1. A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

2. Aytekin C. Ni X. Cricri F. et al.:Clustering and unsupervised anomaly detection with l 2 normalized deep auto‐encoder representations. In:2018 International Joint Conference on Neural Networks (IJCNN) pp. 1–6.IEEE Piscataway NJ(2018)

3. Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

4. Zong B. Song Q. Min M.R. et al.:Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In:International Conference on Learning Representations pp. 1–19.Curran Associates Red Hook NY(2018)

5. Zhang C. Song D. Chen Y. et al.:A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In:Proceedings of the AAAI Conference on Artificial Intelligence pp. 1409–1416.AAAI Washington D.C(2019)

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