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Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder
Daehyung Park, Yuuna Hoshi, and Charles C Kemp · 2018
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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.
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
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Beatgan: Anomalous rhythm detection using adversarially generated time series
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye · 2019
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Usad: Unsupervised anomaly detection on multivariate time series
Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A Zuluaga · 2020
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Rethinking assumptions in deep anomaly detection
Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, and Marius Kloft · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Lifeng Shen, Zhuocong Li, and James Kwok · 2020
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A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A Lozano · 2021
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Neural contextual anomaly detection for time series
Chris U Carmona, François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus · 2021
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Graph neural network-based anomaly detection in multivariate time series
Ailin Deng and Bryan Hooi · 2021
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Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
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Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Rob J Hyndman, and Pablo Montero-Manso · 2021
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An empirical survey of data augmentation for time series classification with neural networks
Brian Kenji Iwana and Seiichi Uchida · 2021
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo · 2021
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
Cited alongside, same era.
Multiresolution knowledge distillation for anomaly detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H Rohban, and Hamid R Rabiee · 2021
Cited alongside, same era.
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn Keogh · 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.
Graph-augmented normalizing flows for anomaly detection of multiple time series
Enyan Dai and Jie Chen · 2022
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
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Yungi Jeong, Eunseok Yang, Jung Hyun Ryu, Imseong Park, and Myungjoo Kang · 2023
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Time-llm: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al · 2023
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Large models for time series and spatio-temporal data: A survey and outlook
Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, et al · 2023
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Unsupervised model selection for time-series anomaly detection
Mononito Goswami, Cristian Challu, Laurent Callot, Lenon Minorics, and Andrey Kan · 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.
Anomaly detection in time series with robust variational quasi-recurrent autoencoders
Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S Jensen · 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.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2022
Cited alongside, same era.
Timesnet: Temporal 2d-variation modeling for general time series analysis
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long · 2022
Cited alongside, same era.
Pull & push: Leveraging differential knowledge distillation for efficient unsupervised anomaly detection and localization
Qihang Zhou, Shibo He, Haoyu Liu, Tao Chen, and Jiming Chen · 2022
Cited alongside, same era.
Staged: A spatial-temporal aware graph encoder-decoder for fault diagnosis in industrial processes
Shizhong Li, Wenchao Meng, Shibo He, Jichao Bi, and Guanglun Liu · 2023
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Prototype-oriented unsupervised anomaly detection for multivariate time series
Yuxin Li, Wenchao Chen, Bo Chen, Dongsheng Wang, Long Tian, and Mingyuan Zhou · 2023
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Memto: Memory-guided transformer for multivariate time series anomaly detection
Junho Song, Keonwoo Kim, Jeonglyul Oh, and Sungzoon Cho · 2023
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Test: Text prototype aligned embedding to activate llm’s ability for time series
Chenxi Sun, Yaliang Li, Hongyan Li, and Shenda Hong · 2023
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Unraveling theanomaly’in time series anomaly detection: A self-supervised tri-domain solution
Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, and Hongzhi Yin · 2023
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Deep contrastive one-class time series anomaly detection
Rui Wang, Chongwei Liu, Xudong Mou, Kai Gao, Xiaohui Guo, Pin Liu, Tianyu Wo, and Xudong Liu · 2023
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Dcdetector: Dual attention contrastive representation learning for time series anomaly detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun · 2023
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Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, et al · 2023
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Detecting multivariate time series anomalies with zero known label
Qihang Zhou, Jiming Chen, Haoyu Liu, Shibo He, and Wenchao Meng · 2023
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One fits all: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
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Label-free multivariate time series anomaly detection
Qihang Zhou, Shibo He, Haoyu Liu, Jiming Chen, and Wenchao Meng · 2024
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