[1] L.F. Shen, Z.C. Li, J.T. Kwok, Timeseries anomaly detection using tempal hierarchical oneclass wk, in: Proc. of the 34th Conf. on Neural Infmation Processing Systems, Red Hook, USA, 2020, pp. 13016–13026.
[3] Thill M., Konen W., Wang H., Bäck T.. Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl. Soft Comput., 112, 107751:1-22(2021).
[4] Krishna K., Murty M.N.. Genetic
[5] M.M. Breunig, H.P. Kriegel, R.T. Ng, J. Ser, LoF: Identifying densitybased local outliers, in: Proc. of ACM SIGMOD Intl. Conf. on Management of Data, Dallas, USA, 2000, pp. 93–104.
[6] Maglaras L.A., Jiang J.-M., Cruz T.J.. Combining ensemble methods and social network metrics for improving accuracy of OCSVM on intrusion detection in SCADA systems. J. Inf. Secur. Appl., 30, 15-26(2016).
[7] F.T. Liu, K.M. Ting, Z.H. Zhou, Isolation Fest, in: Proc. of the 8th IEEE Intl. Conf. on Data Mining, Pisa, Italy, 2008, pp. 413–422.
[9] Xu H., Caramanis C., Mannor S.. Outlier-robust PCA: The high-dimensional case. IEEE T. Inform. Theory, 59, 546-572(2012).
[10] S. Guha, N. Mishra, G. Roy, O. Schrijvers, Robust rom cut fest based anomaly detection on streams, in: Proc. of the 33rd Intl. Conf. on Machine Learning, New Yk, USA, 2016, pp. 2712–2721.
[12] A. Ryzhikov, M. Bisyak, A. Ustyuzhanin, D. Derkach, NFAD: Fixing anomaly detection using nmalizing flows [Online]. Available, https:arxiv.gabs1912.09323, November 2021.
[13] P.F. Marteau, S. SoheilyKhah, N. Béchet, Hybrid Isolation Fest―application to intrusion detection [Online]. Available, https:arxiv.gabs1705.03800, May 2017.
[14] S. Lee, T. Park, K. Lee, Soft contrastive learning f time series, in: Proc. of the 12th Intl. Conf. on Learning Representations, Vienna, Austria, 2024, pp. 1–25.
[15] H.J. Li, H.Z. Xu, W. Peng, C.R. Shen, X.W. Qiu, Multiscale sampling based MLP wks f anomaly detection in multivariate time series, in: Proc. of the IEEE 29th Intl. Conf. on Parallel Distributed Systems, Danzhou, China, 2023, pp. 1421–1428.
[16] C. Lin, J. Liu, K. Katsarou, S. Tahvili, Time series anomaly detection using convolutional neural wks in the manufacturing process of RAN, in: Proc. of IEEE Intl. Conf. on Artificial Intelligence Testing, Athens, Greece, 2023, pp. 90–98.
[17] Y. Su, Y.J. Zhao, C.H. Niu, R. Liu, W. Sun, D. Pei, Robust anomaly detection f multivariate time series through stochastic recurrent neural wk, in: Proc. of the 25th ACM SIGKDD Intl. Conf. on Knowledge Discovery & Data Mining, Anchage, USA, 2019, pp. 2828–2837.
[18] M.L. Shyu, S.C. Chen, K. Sarinnapakn, L.W. Chang, A novel anomaly detection scheme based on principal component classifier, in: Proc. of IEEE Foundations New Directions of Data Mining Wkshop, Piscataway, USA, 2003, pp. 172–179.
[20] J.H. Xu, H.X. Wu, J.M. Wang, M.S. Long, Anomaly transfmer: Time series anomaly detection with association discrepancy, in: Proc. of the 10th Intl. Conf. on Learning Representations, Virtual Event, 2022, pp. 1–20.
[21] Khan Z.A., Hussain T., Ullah A., Rho S., Lee M., Baik S.W.. Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid CNN with a LSTM-AE based framework. Sensors, 20, 1399:1-16(2020).
[22] Z.X. Wang, C.H. Pei, M.H. Ma, et al., Revisiting VAE f unsupervised time series anomaly detection: A frequency perspective, in: Proc. of ACM on Web Conf., Singape, 2024, pp. 3096–3105.
[23] H. Cheng, Q.S. Wen, Y. Liu, L. Sun, RobustTSF: Towards they design of robust time series fecasting with anomalies, in: Proc. of the Twelfth Intl Conf. on Learning Representations, Vienna, Austria, 2024, pp. 1–24.
[25] H.Z. Xu, Y.J. Wang, S.L. Jian, Q. Liao, Y.J. Wang, G.S. Pang, Calibrated oneclass classification f unsupervised time series anomaly detection, IEEE T. Knowl. Data Eng. (2024), doi: 10.1109TKDE.2024.3393996.
[26] Y.H. Zhang, J.C. Yan, Crossfmer: Transfmer utilizing crossdimension dependency f multivariate time series fecasting, in: Proc. of the 11th Intl. Conf. on Learning Representations, Kigali, Rwa, 2023, pp. 1–21.
[28] A. Vaswani, N. Shazeer, N. Parmar, et al., Attention is all you need, in: Proc. of the 31st Intl. Conf. on Neural Infmation Processing Systems, Long Beach, USA, 2017, pp. 6000–6010.
[29] UCI Machine Learning Reposity, KDD Cup 1999 Data [Online]. Available, https:www.kaggle.comdatasetsgalaxyhkddcup1999data, October 1999.
[30] J. Audibert, P. Michiardi, F. Guyard, S. Marti, M.A. Zuluaga, USAD: Unsupervised anomaly detection on multivariate time series, in: Proc. of the 26th ACM SIGKDD Intl. Conf. on Knowledge Discovery & Data Mining, Virtual Event, 2020, pp. 3395–3404.