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Detecting anomalies in multivariate time-series data is essential in many real-world applications.
Outliers in time series
Fox, A. J · 1972
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Time-series novelty detection using one-class support vector machines
Ma, J. and Perkins, S · 2003
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Network anomography
Zhang, Y., Ge, Z., Greenberg, A., and Roughan, M · 2005
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Outlier detection in multivariate time series by projection pursuit
Galeano, P., Peña, D., and Tsay, R. S · 2006
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Outlier detection for temporal data: A survey
Gupta, M., Gao, J., Aggarwal, C. C., and Han, J · 2014
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Sakurada, M. and Yairi, T · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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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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Dilated recurrent neural networks
Chang, S., Zhang, Y., Han, W., Yu, M., Guo, X., Tan, W., Cui, X., Witbrock, M., Hasegawa-Johnson, M. A., and Huang, T. S · 2017
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Anomaly detection in streams with extreme value theory
Siffer, A., Fouque, P.-A., Termier, A., and Largouet, C · 2017
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., and Söderström, T · 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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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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Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications
Xu, H., Chen, W., Zhao, N., Li, Z., Bu, J., Li, Z., Liu, Y., Zhao, Y., Pei, D., Feng, Y., Chen, J., Wang, Z., and Qiao, H · 2018
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
Saito, S., Huang, Z., Natsume, R., Morishima, S., Li, H., and Kanazawa, A · 2019
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., and Pei, D · 2019
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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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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L., Li, Z., and Kwok, J. T · 2020
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Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 2018
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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 · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Mescheder, L. M., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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Occupancy flow: 4d reconstruction by learning particle dynamics
Niemeyer, M., Mescheder, L. M., Oechsle, M., and Geiger, A · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R. A., and Lovegrove, S · 2019
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z
Cited in the paper.
Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H
Cited in the paper.
Sitzmann, V., Martel, J. N. P., Bergman, A. W., Lindell, D. B., and Wetzstein, G · 2020
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A review on outlier/anomaly detection in time series data
Blázquez-García, A., Conde, A., Mori, U., and Lozano, J. A · 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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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Martin-Brualla, R., Radwan, N., Sajjadi, M. S. M., Barron, J. T., Dosovitskiy, A., and Duckworth, D · 2021
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