1.Xin He, Kaiyong Zhao, Xiaowen Chu. (2020). AutoML: A survey of the state-of-the-art. Knowledge-Based Systems, 212, 106622[10.1016/j.knosys.2020.106622]
2.Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton Van Den Hengel. (2019). Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. , 1705–1714[10.1109/iccv.2019.00179]
3.Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei. (2019). Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. , 2828–2837[10.1145/3292500.3330672]
4.Ailin Deng, Bryan Hooi. (2021). Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4027–4035[10.1609/aaai.v35i5.16523]
5.Hyunjong Park, Jongyoun Noh, Bumsub Ham. (2020). Learning Memory-Guided Normality for Anomaly Detection. , 14360–14369[10.1109/cvpr42600.2020.01438]
6.Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings. (2022). TranAD. Proceedings of the VLDB Endowment, 15(6), 1201–1214[10.14778/3514061.3514067]
7.Haowen Xu, Yang Feng, Jie Chen, Zhaogang Wang, Honglin Qiao, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei. (2018). Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications. , 187–196[10.1145/3178876.3185996]
8.Andrew A. Cook, Goksel Misirli, Zhong Fan. (2020). Anomaly Detection for IoT Time-Series Data: A Survey. IEEE Internet of Things Journal, 7(7), 6481–6494[10.1109/jiot.2019.2958185]
10.H. Sorensen, D. Jones, M. Heideman, C. Burrus. (1987). Real-valued fast Fourier transform algorithms. IEEE Transactions on Acoustics Speech and Signal Processing, 35(6), 849–863[10.1109/tassp.1987.1165220]
11.Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, Dan Pei. (2021). Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding. , 3220–3230[10.1145/3447548.3467075]
12.Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura. (2023). Spatio-Temporal Meta-Graph Learning for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 8078–8086[10.1609/aaai.v37i7.25976]
13.Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen. (2019). Outlier Detection for Time Series with Recurrent Autoencoder Ensembles. , 2725–2732[10.24963/ijcai.2019/378]
14.Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. (2020). Spatio-Temporal Graph Structure Learning for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1177–1185[10.1609/aaai.v34i01.5470]
16.Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, Alex Liu. (2022). Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting. , 2362–2368[10.24963/ijcai.2022/328]
17.Xuanhao Chen, Liwei Deng, Yan Zhao, Kai Zheng. (2023). Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation. , 267–275[10.1145/3539597.3570371]
18.Jongsoo Lee, Byeongtae Park, Dong-Kyu Chae. (2023). DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series. , 1188–1197[10.1145/3583780.3614857]
19.Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao. (2020). Multi-source Distributed System Data for AI-Powered Analytics. Lecture notes in computer science, 161–176[10.1007/978-3-030-44769-4_13]
20.Young Geun Kim, Jeong-Han Yun, Siho Han, Hyoung Chun Kim, Simon S. Woo. (2021). Revitalizing Self-Organizing Map: Anomaly Detection Using Forecasting Error Patterns. IFIP advances in information and communication technology, 382–397[10.1007/978-3-030-78120-0_25]
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