Author:
Liu Zhaoyang,Yang Shujie,Ding Zhongyi,Ma Tengchao,Feng Zichen
Reference19 articles.
1. Ren, H., et al.: Time-series anomaly detection service at microsoft. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3009–3017, July 2019
2. Ma, M., Zhang, S., Pei, D., Huang, X., Dai, H.: Robust and rapid adaption for concept drift in software system anomaly detection. In: 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE), pp. 13–24. IEEE, October 2018
3. Gao, J., Song, X., Wen, Q., Wang, P., Sun, L., Xu, H.: RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545 (2020)
4. Karimi, M.: Finite sample AIC for autoregressive model order selection. In: 2007 IEEE International Conference on Signal Processing and Communications, pp. 1219–1222. IEEE, November 2007
5. Badeau, R., David, B.: Weighted maximum likelihood autoregressive and moving average spectrum modeling. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3761–3764. IEEE, March 2008