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We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Support vector method for novelty detection
Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J., Platt, J. C., et al · 1999
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Lof: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
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Fast outlier detection in high dimensional spaces
Angiulli, F. and Pizzuti, C · 2002
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A novel anomaly detection scheme based on principal component classifier
Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., and Chang, L · 2003
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Feature bagging for outlier detection
Lazarevic, A. and Kumar, V · 2005
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, M. U. and Hyvärinen, A · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Long short term memory networks for anomaly detection in time series
Malhotra, P., Vig, L., Shroff, G., and Agarwal, P · 2015
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Librispeech: an asr corpus based on public domain audio books
Panayotov, V., Chen, G., Povey, D., and Khudanpur, S · 2015
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Filonov, P., Lavrentyev, A., and Vorontsov, A · 2016
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A dataset to support research in the design of secure water treatment systems
Goh, J., Adepu, S., Junejo, K. N., and Mathur, A · 2016
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Lstm-based encoder-decoder for multi-sensor anomaly detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., and Shroff, G · 2016
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Swat: A water treatment testbed for research and training on ics security
Mathur, A. P. and Tippenhauer, N. O · 2016
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Variational inference for on-line anomaly detection in high-dimensional time series
Sölch, M., Bayer, J., Ludersdorfer, M., and van der Smagt, P · 2016
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Wadi: a water distribution testbed for research in the design of secure cyber physical systems
Ahmed, C. M., Palleti, V. R., and Mathur, A. P · 2017
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Structured embedding models for grouped data
Rudolph, M., Ruiz, F., Athey, S., and Blei, D · 2017
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Deep anomaly detection using geometric transformations
Golan, I. and El-Yaniv, R · 2018
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Multidimensional time series anomaly detection: A gru-based gaussian mixture variational autoencoder approach
Guo, Y., Liao, W., Wang, Q., Yu, L., Ji, T., and Li, P · 2018
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., and Soderstrom, T · 2018
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Deepant: A deep learning approach for unsupervised anomaly detection in time series
Munir, M., Siddiqui, S. A., Dengel, A., and Ahmed, S · 2018
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Usad: Unsupervised anomaly detection on multivariate time series
Audibert, J., Michiardi, P., Guyard, F., Marti, S., and Zuluaga, M. A · 2020
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Classification-based anomaly detection for general data
Bergman, L. and Hoshen, Y · 2020
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Tadgan: Time series anomaly detection using generative adversarial networks
Geiger, A., Liu, D., Alnegheimish, S., Cuesta-Infante, A., and Veeramachaneni, K · 2020
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Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K · 2020
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Robust unsupervised anomaly detection via multi-time scale dcgans with forgetting mechanism for industrial multivariate time series
Liang, H., Song, L., Wang, J., Guo, L., Li, X., and Liang, J · 2020
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder
Park, D., Hoshi, Y., and Kemp, C. C · 2018
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Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention
Pereira, J. and Silveira, M · 2018
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Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 2018
Cited alongside, same era.
Temporal convolutional networks for anomaly detection in time series
He, Y. and Zhao, J · 2019
Cited alongside, same era.
Lstm-based vae-gan for time-series anomaly detection
Niu, Z., Yu, K., and Wu, X · 2020
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A unifying review of deep and shallow anomaly detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., and Müller, K.-R · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L., Li, Z., and Kwok, J · 2020
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Viewmaker networks: Learning views for unsupervised representation learning
Tamkin, A., Wu, M., and Goodman, N · 2020
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Time series encodings with temporal convolutional networks
Thill, M., Konen, W., and Bäck, T · 2020
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Wu, R. and Keogh, E. J · 2020
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Multivariate time-series anomaly detection via graph attention network
Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., and Zhang, Q · 2020
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Neural contextual anomaly detection for time series
Carmona, C. U., Aubet, F.-X., Flunkert, V., and Gasthaus, J · 2021
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Contrastive predictive coding for anomaly detection
de Haan, P. and Löwe, S · 2021
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Graph neural network-based anomaly detection in multivariate time series
Deng, A. and Hooi, B · 2021
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Revisiting time series outlier detection: Definitions and benchmarks
Lai, K.-H., Zha, D., Xu, J., Zhao, Y., Wang, G., and Hu, X · 2021
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Neural transformation learning for deep anomaly detection beyond images
Qiu, C., Pfrommer, T., Kloft, M., Mandt, S., and Rudolph, M · 2021
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