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Mach. Learn. Sens. Data Anal. (MLSDA)","first-page":"4","article-title":"Anomaly detection using autoencoders with nonlinear dimensionality reduction","author":"Sakurada","year":"Dec. 2014"},{"key":"ref4","series-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","author":"Zong","year":"Apr. 2018"},{"key":"ref5","unstructured":"P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, \u201cLSTM-based encoder-decoder for multi-sensor anomaly detection,\u201d arXiv preprint arXiv:1607.00148, 2016."},{"key":"ref6","unstructured":"D. Li, D. Chen, J. Goh, and S. K. Ng, \u201cAnomaly detection with generative adversarial networks for multivariate time series,\u201d arXiv preprint arXiv:1809.04758, 2018."},{"key":"ref7","series-title":"Proc. Int. Conf. Artif. Neural Netw. (ICANN)","first-page":"703","article-title":"MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks","author":"Li","year":"Sep. 2019"},{"key":"ref8","series-title":"Proc. Knowl. Discov. Data Min. (KDD)","first-page":"2828","article-title":"Robust anomaly detection for multivariate time series through stochastic recurrent neural network","author":"Su","year":"Aug. 2019"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","article-title":"A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder","volume":"3","author":"Park","year":"Feb. 2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref10","series-title":"Proc. KDD, 2022 Workshop Min. Learn. Time Ser.\u2013Deep Forecast.: Models Interpret. Appl.","article-title":"Representation learning using a multi-branch transformer for industrial time series anomaly detection","author":"Zhang","year":"2022"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"S. Tuli, G. Casale, and N. R. Jennings, \u201cTranAD: Deep transformer networks for anomaly detection in multivariate time series data,\u201d arXiv preprint arXiv:2201.07284, 2022.","DOI":"10.14778\/3514061.3514067"},{"key":"ref12","unstructured":"Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, \u201cA time series is worth 64 words: Long-term forecasting with transformers,\u201d arXiv preprint arXiv:2211.14730, 2022."},{"key":"ref13","series-title":"Proc. Knowl. Discov. Data Min. (KDD)","first-page":"3033","article-title":"DCdetector: Dual attention contrastive representation learning for time series anomaly detection","author":"Yang","year":"Aug. 2023"},{"key":"ref14","series-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)","first-page":"4027","article-title":"Graph neural network-based anomaly detection in multivariate time series","author":"Deng","year":"Feb. 2021"},{"key":"ref15","series-title":"Proc. Int. Conf. Mach. Learn. (ICML)","first-page":"153","article-title":"Deep variational graph convolutional recurrent network for multivariate time series anomaly detection","author":"Chen","year":"Jul. 2022"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"103094","DOI":"10.1016\/j.cose.2023.103094","article-title":"GRU-based interpretable multivariate time series anomaly detection in industrial control system","volume":"127","author":"Tang","year":"Apr. 2023","journal-title":"Comput. Secur."},{"key":"ref17","series-title":"Proc. Euro. Conf. Princ. Data Min. Knowl. Discov. (ECML-PKDD)","first-page":"15","article-title":"Fast outlier detection in high dimensional spaces","author":"Angiulli","year":"Aug. 2002"},{"key":"ref18","series-title":"Proc. IEEE Foundations New Dir. Data Min. Workshop","first-page":"172","article-title":"A novel anomaly detection scheme based on principal component classifier","author":"Shyu","year":"2003"},{"key":"ref19","series-title":"Proc. Special Interest Group Manag. Data (SIGMOD)","first-page":"93","article-title":"LOF: Identifying density-based local outliers","author":"Breunig","year":"May 2000"},{"key":"ref20","first-page":"1","article-title":"Network anomaly detection based on wavelet analysis","volume":"2009","author":"Lu","year":"2009","journal-title":"Eurasip J. Adv. Sig. Process."},{"key":"ref21","first-page":"3835","article-title":"Wavelet based detection of outliers in volatility time series models","volume":"72","author":"Rashedi","year":"Mar. 2022","journal-title":"Comput. Mater. Contin."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Sch\u00f6lkopf","year":"Jul. 2001","journal-title":"Neural Comput."},{"key":"ref23","series-title":"Proc. Int. Conf. Commun. Softw. Netw. (ICCSN)","first-page":"205","article-title":"Arima based network anomaly detection","author":"Yaacob","year":"Mar. 2010"},{"key":"ref24","series-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)","first-page":"1409","article-title":"A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data","author":"Zhang","year":"Jan. 2019"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"Jan. 2019","journal-title":"Neurocomputing"},{"key":"ref26","first-page":"1713","article-title":"Defect detection model using time series data augmentation and transformation","volume":"78","author":"Kim","year":"Feb. 2024","journal-title":"Comput. Mater. Contin."},{"key":"ref27","unstructured":"P. Filonov, A. Lavrentyev, and A. Vorontsov, \u201cMultivariate industrial time series with cyber-attack simulation: Fault detection using an LSTM-based predictive data model,\u201d arXiv preprint arXiv:1612.06676, 2016."},{"key":"ref28","series-title":"Proc. Knowl. Discov. Data Min. (KDD)","first-page":"387","article-title":"Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding","author":"Hundman","year":"Aug. 2018"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TKDE.2020.3014806","article-title":"Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional LSTM-CNN","volume":"34","author":"Liu","year":"Jun. 2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref30","series-title":"Proc. Knowl. Discov. Data Min. (KDD)","first-page":"3395","article-title":"USAD: Unsupervised anomaly detection on multivariate time series","author":"Audibert","year":"Aug. 2020"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"12068","DOI":"10.1109\/TKDE.2021.3139916","article-title":"Adaptive memory networks with self-supervised learning for unsupervised anomaly detection","volume":"35","author":"Zhang","year":"Dec. 2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref32","unstructured":"A. Dosovitskiy et al., \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d  arXiv preprint arXiv:2010.11929, 2010."},{"key":"ref33","series-title":"Proc. Annu. Conf. Neural Inform. Process. Syst. (NIPS)","first-page":"5754","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","author":"Yang","year":"Dec. 2019"},{"key":"ref34","unstructured":"J. Xu, H. Wu, J. Wang, and M. 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J."},{"key":"ref40","doi-asserted-by":"crossref","first-page":"7624","DOI":"10.1109\/TII.2022.3216006","article-title":"Learning hierarchical spatial-temporal graph representations for robust multivariate industrial anomaly detection","volume":"19","author":"Yang","year":"Oct. 2023","journal-title":"IEEE Trans. Ind. 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