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Time series anomaly detection plays a vital role in a wide range of applications.
Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information
S. D. Greenwald, Ramesh S. Patil, and Roger G. Mark · 1990
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Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
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Support vector method for novelty detection
Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, and John C. Platt · 1999
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LOF: identifying density-based local outliers
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander · 2000
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The impact of the mit-bih arrhythmia database
George B. Moody and Roger G. Mark · 2001
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A novel anomaly detection scheme based on principal component classifier
Mei-Ling Shyu, Shu-Ching Chen, Kanoksri Sarinnapakorn, and Liwu Chang · 2003
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Collecting complex activity datasets in highly rich networked sensor environments
Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian Förster, Gerhard Tröster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, Jakob Doppler, Clemens Holzmann, Marc Kurz, Gerald Holl, Ricardo Chavarriaga, Hesam Sagha, Hamidreza Bayati, Marco Creatura, and José del R. Millán · 2010
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Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
Markus Goldstein and Andreas Dengel · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky · 2015
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S5-a labeled anomaly detection dataset, version 1.0 (16m), 2015
N Laptev, S Amizadeh, and Y Billawala · 2015
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A comparative study of HTM and other neural network models for online sequence learning with streaming data
Yuwei Cui, Chetan Surpur, Subutai Ahmad, and Jeff Hawkins · 2016
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Swat: a water treatment testbed for research and training on ICS security
Aditya P. Mathur and Nils Ole Tippenhauer · 2016
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Loda: Lightweight on-line detector of anomalies
Tomás Pevný · 2016
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Matrix profile I: all pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets
Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn J. Keogh · 2016
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Anomaly detection in finance: Editors’ introduction
Archana Anandakrishnan, Senthil Kumar, Alexander R. Statnikov, Tanveer A. Faruquie, and Di Xu · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Anomaly detection in streams with extreme value theory
Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët · 2017
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Söderström · 2018
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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 autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Dae-ki Cho, and Haifeng Chen · 2018
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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
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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
Cited alongside, same era.
Time-series anomaly detection service at microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang · 2019
Cited alongside, same era.
f-anogan: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Beatgan: Anomalous rhythm detection using adversarially generated time series
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye · 2019
Cited alongside, same era.
A comparative study on unsupervised anomaly detection for time series: Experiments and analysis
Yan Zhao, Liwei Deng, Xuanhao Chen, Chenjuan Guo, Bin Yang, Tung Kieu, Feiteng Huang, Torben Bach Pedersen, Kai Zheng, and Christian S. Jensen · 2022
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Forecastpfn: Synthetically-trained zero-shot forecasting
Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha V. Naidu, and Colin White · 2023
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Timegpt-1
Azul Garza and Max Mergenthaler Canseco · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
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How does information bottleneck help deep learning?
Kenji Kawaguchi, Zhun Deng, Xu Ji, and Jiaoyang Huang · 2023
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Time series contrastive learning with information-aware augmentations
Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Yuncong Chen, Haifeng Chen, and Xiang Zhang · 2023
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Anomaly detection for iot time-series data: A survey
Andrew A. Cook, Goksel Misirli, and Zhong Fan · 2020
Cited alongside, same era.
Timeseries anomaly detection using temporal hierarchical one-class network
Lifeng Shen, Zhuocong Li, and James T. Kwok · 2020
Cited alongside, same era.
Markusthill/mgab: The mackey-glass anomaly benchmark (version v1.0.1)
Markus Thill, Wolfgang Konen, and Thomas Bäck · 2020
Cited alongside, same era.
Practical approach to asynchronous multivariate time series anomaly detection and localization
Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki · 2021
Cited alongside, same era.
Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, and Pablo Montero-Manso · 2021
Cited alongside, same era.
Exathlon: A benchmark for explainable anomaly detection over time series
Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, and Nesime Tatbul · 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 Ben Hu · 2021
Cited alongside, same era.
Later among the works it cites.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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Magicscaler: Uncertainty-aware, predictive autoscaling
Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Yunyao Cheng, Peng Chen, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, et al · 2023
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn J. Keogh · 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, and Shirui Pan · 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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ModernTCN: A modern pure convolution structure for general time series analysis
Luo donghao and wang xue · 2024
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Sensitivehue: Multivariate time series anomaly detection by enhancing the sensitivity to normal patterns
Yuye Feng, Wei Zhang, Yao Fu, Weihao Jiang, Jiang Zhu, and Wenqi Ren · 2024
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MOMENT: A family of open time-series foundation models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski · 2024
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Shiyan Hu, Kai Zhao, Xiangfei Qiu, Yang Shu, Jilin Hu, Bin Yang, and Chenjuan Guo · 2024
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Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, and Bin Yang · 2024
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Autotsad: Unsupervised holistic anomaly detection for time series data
Sebastian Schmidl, Felix Naumann, and Thorsten Papenbrock · 2024
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Memto: Memory-guided transformer for multivariate time series anomaly detection
Junho Song, Keonwoo Kim, Jeonglyul Oh, and Sungzoon Cho · 2024
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Air quality prediction with physics-informed dual neural odes in open systems
Jindong Tian, Yuxuan Liang, Ronghui Xu, Peng Chen, Chenjuan Guo, Aoying Zhou, Lujia Pan, Zhongwen Rao, and Bin Yang · 2024
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Rose: Register assisted general time series forecasting with decomposed frequency learning
Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, and Chenjuan Guo · 2024
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Unified training of universal time series forecasting transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo · 2024
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Catch: Channel-aware multivariate time series anomaly detection via frequency patching
Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, and Bin Yang · 2025
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