[2]
N. Laptev, S. Amizadeh, and I. Flint, Generic and scalable framework for automated time-series anomaly detection, in Proc. 21th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 1939–1947.
[3]
Z. Li, X. Xu, T. Hang, H. Xiang, Y. Cui, L. Qi, and X. Zhou, A knowledge-driven anomaly detection framework for social production system, IEEE Trans. Comput. Soc. Syst. doi: 10.1109/TCSS.2022.3217790.
[13]
H. Z. Moayedi and M. A. Masnadi-Shirazi, ARIMA model for network traffic prediction and anomaly detection, in Proc. 2008 Int. Symp. Information Technology, Kuala Lumpur, Malaysia, 2008, pp. 1–6.
[14]
F. Knorn and D. J. Leith, Adaptive Kalman filtering for anomaly detection in software appliances, in Proc. IEEE INFOCOM Workshops, Phoenix, AZ, USA, 2008, pp. 1–6.
[15]
M. Amer, M. Goldstein, and S. Abdennadher, Enhancing one-class support vector machines for unsupervised anomaly detection, in Proc. ACM SIGKDD Workshop on Outlier Detection and Description, Chicago, IL, USA, 2013, pp. 8–15.
[18]
F. Angiulli and C. Pizzuti, Fast outlier detection in high dimensional spaces, in Proc. 6th European Conf. Principles of Data Mining and Knowledge Discovery, 2002, Helsinki, Finland, 2002, pp. 15–27.
[19]
R. Zhang, S. Dong, X. Nie, and S. Xiao, Feedforward neural network for time series anomaly detection, arXiv preprint arXiv: 1812.08389, 2018.
[22]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, in Proc. 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 6000–6010.
[24]
P. Malhotra, L. Vig, G. M. Shroff, and P. Agarwal, Long short term memory networks for anomaly detection in time series, in Proc. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2015, pp. 89–94.
[26]
C. C. Aggarwal, Time series and multidimensional streaming outlier detection, in Outlier Analysis, C. C. Aggarwal, ed. Cham, Switzerland: Springer, 2017, pp. 273–310.
[27]
H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al., Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications, in Proc. 2018 World Wide Web Conf., Lyon, France, 2018, pp. 187–196.
[29]
D. Li, D. Chen, B. Jin, L. Shi, J. Goh, and S. K. Ng, MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks, in Proc. 28th Int. Conf. Artificial Neural Networks, Munich, Germany, 2019, pp. 703–716.
[30]
J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga, USAD: Unsupervised anomaly detection on multivariate time series, in Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020, pp. 3395–3404.
[34]
D. Liu, Y. Zhao, H. Xu, Y. Sun, D. Pei, J. Luo, X. Jing, and M. Feng, Opprentice: Towards practical and automatic anomaly detection through machine learning, in Proc. 2015 Internet Measurement Conference, Tokyo, Japan, 2015, pp. 211–224.
[35]
W. Qiu, Y. Wu, G. Wang, S. X. Yang, J. Bai, and J. Li, A novel unsupervised anomaly detection based on robust principal component classifier, in Proc. Defense and Security Symposium, Orlando (Kissimmee), FL, USA, 2006, pp. 260–268.
[37]
Y. Yuan, J. Yu, X. Cheng, Z. Zou, D. Yu, and Z. Cai, Decentralized parallel SGD based on weight-balancing for intelligent IoV, IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3216709.
[40]
S. Chen, D. Yu, F. Li, Z. Zou, W. Liang, and X. Cheng, PPAR: A privacy-preserving adaptive ranking algorithm for multi-armed-bandit crowdsourcing, in Proc. 2022 IEEE/ACM 30th Int. Symp. Quality of Service (IWQoS), Oslo, Norway, 2022, pp. 1–10.
[45]
A. Ihler, J. Hutchins, and P. Smyth, Adaptive event detection with time-varying Poisson processes, in Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 2006, pp. 207–216.