D.Y. Oh, I. Yun, Residual error based anomaly detection using auto-encoder in smd machine sound. Sensors **18**, 1308 (2018). https://doi.org/10.3390/s18051308

Article
Google Scholar

S. Fuertes, G. Picart, J.-Y. Tourneret, L. Chaâri, A. Ferrari, C. Richard, Improving spacecraft health monitoring with automatic anomaly detection techniques. (2016)

Y. Hagiwara, H. Fujita, S.L. Oh, J.H. Tan, R.S. Tan, E.J. Ciaccio, U.R. Acharya, Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review. Inf. Sci. **467**, 99–114 (2018). https://doi.org/10.1016/j.ins.2018.07.063

Article
Google Scholar

A. Grane, H. Veiga, Wavelet-based detection of outliers in financial time series. Comput. Stat. Data Anal. **54**, 2580 (2010). https://doi.org/10.1016/j.csda.2009.12.010

Article
MathSciNet
MATH
Google Scholar

A. Siffer, P.-A. Fouque, A. Termier, C. Largouet, Anomaly detection in streams with extreme value theory. in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’17, (Association for Computing Machinery, New York 2017), pp. 1067–1075. https://doi.org/10.1145/3097983.3098144

R.G. Cirstea, T. Kieu, C. Guo, B. Yang, S. Pan, Enhancenet: plugin neural networks for enhancing correlated time series forecasting, (2021) pp. 1739–1750. https://doi.org/10.1109/ICDE51399.2021.00153

J. Hu, B. Yang, C. Guo, C.S. Jensen, Risk-aware path selection with time-varying, uncertain travel costs: a time series approach. VLDB J. **27**(2), 179–200 (2018). https://doi.org/10.1007/s00778-018-0494-9

Article
Google Scholar

J.C.M. Oliveira, K.V. Pontes, I. Sartori, M. Embiruçu, Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Syst. Appl. **84**, 200–219 (2017). https://doi.org/10.1016/j.eswa.2017.05.020

Article
Google Scholar

P. Rajpurkar, A. Hannun, M. Haghpanahi, C. Bourn, A. Ng, Cardiologist-level arrhythmia detection with convolutional neural networks (2017)

B. Zhou, S. Liu, B. Hooi, X. Cheng, J. Ye, Beatgan: Anomalous rhythm detection using adversarially generated time series, (2019) pp. 4433–4439. https://doi.org/10.24963/ijcai.2019/616

L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S.A. Siddiqui, A. Binder, E. Müller, M. Kloft, Deep one-class classification. ed. by J. Dy, A. Krause (eds.) in *Proceedings of the 35th international conference on machine learning*. Proceedings of Machine Learning Research, vol. 80, (PMLR, 2018) pp. 4393–4402. https://proceedings.mlr.press/v80/ruff18a.html

Z. Li, Y. Zhao, J. Han, Y. Su, R. Jiao, X. Wen, D. Pei, Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. in *Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining*. KDD ’21, (Association for Computing Machinery, New York, 2021) pp. 3220–3230. https://doi.org/10.1145/3447548.3467075

J. An, S. Cho, Variational autoencoder based anomaly detection using reconstruction probability. (2015)

H. Wold, A study in analysis of stationary time series. J. R. Stat. Soc. **102**(2), 295–298 (1938)

MATH
Google Scholar

C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, N.V. Chawla, N.V, A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. in *Proceedings of the thirty-third AAAI conference on artificial intelligence and thirty-first innovative applications of artificial intelligence conference and ninth AAAI symposium on educational advances in artificial intelligence*. AAAI’19/IAAI’19/EAAI’19. (AAAI Press, 2019). https://doi.org/10.1609/aaai.v33i01.33011409

I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets. in *Proceedings of the 27th international conference on neural information processing systems*, Vol. 2. NIPS’14, (MIT Press, Cambridge, MA, 2014) pp. 2672–2680

T. Schlegl, P. Seeböck, S.M. Waldstein, U. Schmidt-Erfurth, G. Langs, Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, in *Information Processing in Medical Imaging*. ed. by M. Niethammer, M. Styner, S. Aylward, H. Zhu, I. Oguz, P.-T. Yap, D. Shen (Springer, Cham, 2017), pp.146–157

Chapter
Google Scholar

M. Arjovsky, L. Bottou, Towards principled methods for training generative adversarial networks. stat **1050** (2017)

J. Audibert, P. Michiardi, F. Guyard, S. Marti, M.A. Zuluaga, Usad: unsupervised anomaly detection on multivariate time series. KDD ’20, (Association for Computing Machinery, New York, 2020) pp. 3395–3404. https://doi.org/10.1145/3394486.3403392

M. Sakurada, T. Yairi, Anomaly detection using autoencoders with nonlinear dimensionality reduction. MLSDA’14, (Association for Computing Machinery, New York, 2014) pp. 4–11. https://doi.org/10.1145/2689746.2689747

P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, G. Shroff, Lstm-based encoder-decoder for multi-sensor anomaly detection (2016)

M. Gutoski, M. Romero Aquino, M. Ribeiro, A. Lazzaretti, H. Lopes, Detection of video anomalies using convolutional autoencoders and one-class support vector machines. (2017). https://doi.org/10.21528/CBIC2017-49

C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, N.V. Chawla, A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. in *Proceedings of the thirty-third AAAI conference on artificial intelligence and thirty-first innovative applications of artificial intelligence conference and ninth AAAI symposium on educational advances in artificial intelligence*. AAAI’19/IAAI’19/EAAI’19. (AAAI Press, 2019). https://doi.org/10.1609/aaai.v33i01.33011409

H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, J. Chen, Z. Wang, H. Qiao, Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. in *Proceedings of the 2018 world wide web conference*. WWW ’18, (International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 2018) pp. 187–196. https://doi.org/10.1145/3178876.3185996

D. Xu, Y. Yan, E. Ricci, N. Sebe, Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput. Vis. Image Understand. **156**, 117–127 (2017). https://doi.org/10.1016/j.cviu.2016.10.010. Image and Video Understanding in Big Data

D. Wulsin, J. Blanco, R. Mani, B. Litt, Semi-supervised anomaly detection for EEG waveforms using deep belief nets. in *2010 Ninth international conference on machine learning and applications*, (2010) pp. 436–441. https://doi.org/10.1109/ICMLA.2010.71

T. Schlegl, P. Seeböck, S.M. Waldstein, U. Schmidt-Erfurth, G. Langs, Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, in *Information processing in medical imaging*. ed. by M. Niethammer, M. Styner, S. Aylward, H. Zhu, I. Oguz, P.-T. Yap, D. Shen (Springer, Cham, 2017), pp.146–157

Chapter
Google Scholar

S. Akcay, A. Atapour-Abarghouei, T.P. Breckon, Ganomaly: semi-supervised anomaly detection via adversarial training, in *Computer vision - ACCV 2018*. ed. by C.V. Jawahar, H. Li, G. Mori, K. Schindler (Springer, Cham, 2019), pp.622–637

Chapter
Google Scholar

D. Li, D. Chen, B. Jin, L. Shi, J. Goh, S.-K. Ng, Mad-Gan: multivariate anomaly detection for time series data with generative adversarial networks, in *Artificial neural networks and machine learning–ICANN 2019: text and time series*. ed. by I.V. Tetko, V. Kůrková, P. Karpov, F. Theis (Springer, Cham, 2019), pp.703–716

Chapter
Google Scholar

D. Shipmon, J. Gurevitch, P. Piselli, S. Edwards, Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data (2017)

E. Keogh, J. Lin, A. Fu, ECG and 2d gesture dataset. (2005). https://www.cs.ucr.edu/~eamonn/discords/

P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, G. Shroff, Lstm-based encoder-decoder for multi-sensor anomaly detection (2016)

Y.-H. Yoo, U.-H. Kim, J.-H. Kim, Recurrent reconstructive network for sequential anomaly detection. IEEE Trans. Cybern **51**(3), 1704–1715 (2021). https://doi.org/10.1109/TCYB.2019.2933548

Article
Google Scholar

T. Kieu, B. Yang, C.S. Jensen, Outlier detection for multidimensional time series using deep neural networks. in *2018 19th IEEE international conference on mobile data management (MDM)*, (2018) pp. 125–134. https://doi.org/10.1109/MDM.2018.00029