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In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD.
Deep learning for anomaly detection: A survey
Chalapathy, R.; and Chawla, S. 2019 · 1901
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
Time-series novelty detection using one-class support vector machines
Ma, J.; and Perkins, S. 2003 · 2003
Earlier work this paper cites.
Cyber physical systems: Design challenges
Lee, E. A. 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
Earlier work this paper cites.
Cyber-physical systems
Baheti, R.; and Gill, H. 2011 · 2011
Earlier work this paper cites.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; and Zheng, X. 2015 · 2015
Earlier work this paper cites.
A cyber-physical systems architecture for industry 4.0-based manufacturing systems
Lee, J.; Bagheri, B.; and Kao, H.-A. 2015 · 2015
Earlier work this paper cites.
A dataset to support research in the design of secure water treatment systems
Goh, J.; Adepu, S.; Junejo, K. N.; and Mathur, A. 2016 · 2016
Earlier work this paper cites.
LSTM-based encoder-decoder for multi-sensor anomaly detection
Malhotra, P.; Ramakrishnan, A.; Anand, G.; Vig, L.; Agarwal, P.; and Shroff, G. 2016 · 2016
Earlier work this paper cites.
SWaT: a water treatment testbed for research and training on ICS security
Mathur, A. P.; and Tippenhauer, N. O. 2016 · 2016
Cited alongside, same era.
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 · 2017
Cited alongside, same era.
RADM: real-time anomaly detection in multivariate time series based on Bayesian network
Ding, N.; Gao, H.; Bu, H.; and Ma, H. 2018 · 2018
Cited alongside, same era.
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Hundman, K.; Constantinou, V.; Laporte, C.; Colwell, I.; and Soderstrom, T. 2018 · 2018
Cited alongside, same era.
A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder
Park, D.; Hoshi, Y.; and Kemp, C. C. 2018 · 2018
Cited alongside, same era.
BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series
Zhou, B.; Liu, S.; Hooi, B.; Cheng, X.; and Ye, J. 2019 · 2019
Later among the works it cites.
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Audibert, J.; Michiardi, P.; Guyard, F.; Marti, S.; and Zuluaga, M. A. 2020 · 2020
Later among the works it cites.
Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L.; Li, Z.; and Kwok, J. 2020 · 2020
Later among the works it cites.
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
Wu, W.; He, L.; Lin, W.; Su, Y.; Cui, Y.; Maple, C.; and Jarvis, S. A. 2020 · 2020
Later among the works it cites.
Patch svdd: Patch-level svdd for anomaly detection and segmentation
Yi, J.; and Yoon, S. 2020 · 2020
Later among the works it cites.
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
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Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Xu, H.; Feng, Y.; Chen, J.; Wang, Z.; Qiao, H.; Chen, W.; Zhao, N.; Li, Z.; Bu, J.; Li, Z.; and et al. 2018 · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Zhang, C.; Song, D.; Chen, Y.; Feng, X.; Lumezanu, C.; Cheng, W.; Ni, J.; Zong, B.; Chen, H.; and Chawla, N. V. 2019 · 2019
Cited alongside, same era.
Chen, Z.; Chen, D.; Zhang, X.; Yuan, Z.; and Cheng, X. 2021 · 2021
Closest in time.
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Choi, K.; Yi, J.; Park, C.; and Yoon, S. 2021 · 2021
Closest in time.
Graph neural network-based anomaly detection in multivariate time series
Deng, A.; and Hooi, B. 2021 · 2021
Closest in time.
Revisiting Time Series Outlier Detection: Definitions and Benchmarks
Lai, K.-H.; Zha, D.; Xu, J.; Zhao, Y.; Wang, G.; and Hu, X. 2021 · 2021
Closest in time.
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Wu, R.; and Keogh, E. 2021 · 2021
Closest in time.