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Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade.
“A dataset to support research in the design of secure water treatment systems,”
J. Goh, S. Adepu, K. N. Junejo, and A. Mathur, · 2016
Earlier work this paper cites.
“Robust anomaly detection for multivariate time series through stochastic recurrent neural network,”
Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, · 2019
Earlier work this paper cites.
“Multivariate time-series anomaly detection via graph attention network,”
H. Zhao, Y. Wang, J. Duan, C. Huang, D. Cao, Y. Tong, B. Xu, J. Bai, J. Tong, and Q. Zhang, · 2020
Earlier work this paper cites.
“Usad: Unsupervised anomaly detection on multivariate time series,”
Julien Audibert, Pietro Michiardi, Francis Guyard, Stephan Marti, and Marc A. Zuluaga, · 2020
Earlier work this paper cites.
“Deep learning for time series forecasting: A survey,”
José F. Torres, Dalil Hadjout, Abderrazak Sebaa, Francisco Martínez-Álvarez, and Alicia Troncoso, · 2021
Earlier work this paper cites.
“Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines,”
Kukjin Choi, Jihun Yi, Changhwa Park, and Sungroh Yoon, · 2021
Cited alongside, same era.
“Graph neural network-based anomaly detection in multivariate time series,”
Ailin Deng and Bryan Hooi, · 2021
Cited alongside, same era.
“Self-supervised acoustic anomaly detection via contrastive learning,”
Hadi Hojjati and Narges Armanfard, · 2022
Cited alongside, same era.
“An evaluation of anomaly detection and diagnosis in multivariate time series,”
Astha Garg, Wenyu Zhang, Jules Samaran, Ramasamy Savitha, and Chuan-Sheng Foo, · 2022
Cited alongside, same era.
“Towards a rigorous evaluation of time-series anomaly detection,”
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon, · 2022
Cited alongside, same era.
“Multivariate time-series anomaly detection with temporal self-supervision and graphs: Application to vehicle failure prediction,”
Hadi Hojjati, Mohammadreza Sadeghi, and Narges Armanfard, · 2023
Later among the works it cites.
“Self-supervised learning for anomalous channel detection in eeg graphs: Application to seizure analysis,”
Thi Kieu Khanh Ho and Narges Armanfard, · 2023
Later among the works it cites.
“Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress,”
Renjie Wu and Eamonn J. Keogh, · 2023
Later among the works it cites.
“Open-set multivariate time-series anomaly detection,”
T. Lai, T. K. K. Ho, and N. Armanfard, · 2024
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