dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

Chauhan, S., Vig, L.: Anomaly detection in ECG time signals via deep long short-term memory networks. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics, pp. 1–7 (2015)

Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The ucr time series classification archive (2015). https://www.cs.ucr.edu/~eamonn/time_series_data/

Chuah, M.C., Fu, F.: ECG anomaly detection via time series analysis. In: Proceedings of International Workshops on Frontiers of High Performance Computing and Networking, pp. 123–135 (2007)

Gaddam, S.R., Phoha, V.V., Balagani, K.S.: K-means+ ID3: a novel method for supervised anomaly detection by cascading k-means clustering and ID3 decision tree learning methods. IEEE Trans. Knowl. Data Eng. 19(3), 345–354 (2007)

Hayton, P., Utete, S., King, D., King, S., Anuzis, P., Tarassenko, L.: Static and dynamic novelty detection methods for jet engine health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 493–514 (2007)

Hill, D.J., Minsker, B.S.: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach. Environ. Model. Softw. 25(9), 1014–1022 (2010)

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980

Kingma, D., Welling, M.: Auto-encoding variational Bayes. In: Proceedings of the 2nd International Conference on Learning Representations(ICLR) (2014)

Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16(1), 1–20 (2016)

Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shrof, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection. In: Presented at ICML 2016 Anomaly Detection Workshop (2016). arXiv preprint arXiv:1607.00148

Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. Proc. Eur. Symp. Artif. Neural Netw. 23, 89–94 (2015)

Maruyama, Y., Yamanishi, K.: Dynamic model selection with its applications to computer security. In: Information Theory Workshop, pp. 82–87 (2004). https://doi.org/10.1109/ITW.2004.1405279

Miyaguchi, K., Yamanishi, K.: Online detection of continuous changes in stochastic processes. Int. J. Data Sci. Anal. 3(3), 213–229 (2017)

Rayana, S., Akoglu, L.: Less is more: building selective anomaly ensembles. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 42 (2016)

Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 833–840 (2011)

Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Proceedings of the Annual Conference of International Speech Communication Association, pp. 338–342 (2014)

Sakurada, M., Yairia, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)

Saligrama, F.N.V.: Dynamic model selection for prediction under a budget (2017). arXiv preprint arXiv:1704.07505

Sivaraks, H., Ratanamahatana, C.A.: Robust and accurate anomaly detection in ECG artifacts using time series motif discovery. Comput. Math. Methods Med. 2015, 1–20 (2015)

Söelch, M., Bayer, J., Ludersdorfer, M., van der Smagt, P.: Variational inference for on-line anomaly detection in high-dimensional time series (2016). arXiv preprint arXiv:1602.07109

Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

Takeuchi, J., Yamanishi, K.: a unifying framework for detecting outliers and change points from non-stationary time series data. IEEE Trans. Knowl. Data Eng. 18(4), 482–492 (2006)