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Many methods have been proposed for unsupervised time series anomaly detection.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom · 2018
Earlier work this paper cites.
Gecco 2018 industrial challenge: Monitoring of drinking-water quality
Frederik Rehbach, Steffen Moritz, Sowmya Chandrasekaran, Margarita Rebolledo, Martina Friese, and Thomas Bartz-Beielstein · 2018
Earlier work this paper cites.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Earlier work this paper cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
Earlier work this paper cites.
Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks
Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei · 2019
Earlier work this paper cites.
Usad: Unsupervised anomaly detection on multivariate time series
Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A Zuluaga · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Earlier work this paper cites.
Timeseries anomaly detection using temporal hierarchical one-class network
Lifeng Shen, Zhuocong Li, and James Kwok · 2020
Earlier work this paper cites.
Multivariate time-series anomaly detection via graph attention network
Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang · 2020
Earlier work this paper cites.
Practical approach to asynchronous multivariate time series anomaly detection and localization
Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki · 2021
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Cited alongside, same era.
Gan-based anomaly detection for multivariate time series using polluted training set
Bowen Du, Xuanxuan Sun, Junchen Ye, Ke Cheng, Jingyuan Wang, and Leilei Sun · 2021
Cited alongside, same era.
Revisiting time series outlier detection: Definitions and benchmarks
Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu · 2021
Cited alongside, same era.
Anomaly transformer: Time series anomaly detection with association discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2021
Cited alongside, same era.
Timeeval: A benchmarking toolkit for time series anomaly detection algorithms
Phillip Wenig, Sebastian Schmidl, and Thorsten Papenbrock · 2022
Later among the works it cites.
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi · 2022
Later among the works it cites.
Time series based data explorer and stream analysis for anomaly prediction
Xiao-Xia Yin, Yuan Miao, Yanchun Zhang, et al · 2022
Later among the works it cites.
Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
Later among the works it cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Later among the works it cites.
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Unsupervised time series outlier detection with diversity-driven convolutional ensembles
David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S. Jensen · 2022
Cited alongside, same era.
Deep generative model with hierarchical latent factors for time series anomaly detection
Cristian I Challu, Peihong Jiang, Ying Nian Wu, and Laurent Callot · 2022
Cited alongside, same era.
Local evaluation of time series anomaly detection algorithms
Alexis Huet, Jose Manuel Navarro, and Dario Rossi · 2022
Cited alongside, same era.
Towards a rigorous evaluation of time-series anomaly detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon · 2022
Cited alongside, same era.
Learning robust deep state space for unsupervised anomaly detection in contaminated time-series
Longyuan Li, Junchi Yan, Qingsong Wen, Yaohui Jin, and Xiaokang Yang · 2022
Cited alongside, same era.
Anomaly detection in time series: a comprehensive evaluation
Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock · 2022
Cited alongside, same era.
Robust time series analysis and applications: An industrial perspective
Qingsong Wen, Linxiao Yang, Tian Zhou, and Liang Sun · 2022
Cited alongside, same era.
Precursor-of-anomaly detection for irregular time series
Sheo Yon Jhin, Jaehoon Lee, and Noseong Park · 2023
Later among the works it cites.
Soft contrastive learning for time series
Seunghan Lee, Taeyoung Park, and Kibok Lee · 2023
Later among the works it cites.
Dcdetector: Dual attention contrastive representation learning for time series anomaly detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun · 2023
Later among the works it cites.
Memto: Memory-guided transformer for multivariate time series anomaly detection
Junho Song, Keonwoo Kim, Jeonglyul Oh, and Sungzoon Cho · 2024
Closest in time.
Drift doesn’t matter: Dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection
Chengsen Wang, Zirui Zhuang, Qi Qi, Jingyu Wang, Xingyu Wang, Haifeng Sun, and Jianxin Liao · 2024
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Anomaly prediction: A novel approach with explicit delay and horizon
Jiang You, Arben Cela, René Natowicz, Jacob Ouanounou, and Patrick Siarry · 2024
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One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al · 2024
Closest in time.