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Time-series anomaly detection is a popular topic in both academia and industrial fields.
Generic and Scalable Framework for Automated Time-series Anomaly Detection. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, New York, NY, USA, 1939–1947
Nikolay Laptev, Saeed Amizadeh, and Ian Flint. 2015 · 1947
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The Holt-winters forecasting procedure
Chris Chatfield. 1978 · 1978
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Percentage points for a generalized ESD many-outlier procedure
Bernard Rosner. 1983 · 1983
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Classification and regression by randomForest
Andy Liaw, Matthew Wiener, et al · 2002
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Network anomography. In Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement . 30–30
Yin Zhang, Zihui Ge, Albert Greenberg, and Matthew Roughan. 2005 · 2005
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Saliency detection: A spectral residual approach. In 2007 IEEE Conference on computer vision and pattern recognition . IEEE, 1–8
Xiaodi Hou and Liqing Zhang. 2007 · 2007
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Network anomaly detection based on wavelet analysis
Wei Lu and Ali A Ghorbani. 2008 · 2008
Cited alongside, same era.
Rapid detection of maintenance induced changes in service performance. In Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies . 1–12
Ajay Mahimkar, Zihui Ge, Jia Wang, Jennifer Yates, Yin Zhang, Joanne Emmons, Brian Huntley, and Mark Stockert. 2011 · 2011
Cited alongside, same era.
Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
Markus Goldstein and Andreas Dengel. 2012 · 2012
Cited alongside, same era.
Opprentice: Towards practical and automatic anomaly detection through machine learning. In Proceedings of the 2015 Internet Measurement Conference . 211–224
Dapeng Liu, Youjian Zhao, Haowen Xu, Yongqian Sun, Dan Pei, Jiao Luo, Xiaowei Jing, and Mei Feng. 2015 · 2015
Cited alongside, same era.
Sequential ensemble learning for outlier detection: A bias-variance perspective. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 1167–1172
Shebuti Rayana, Wen Zhong, and Leman Akoglu. 2016 · 2016
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Outlier detection with autoencoder ensembles. In Proceedings of the 2017 SIAM international conference on data mining . SIAM, 90–98
Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga. 2017 · 2017
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Automatic anomaly detection in the cloud via statistical learning
Jordan Hochenbaum, Owen S Vallis, and Arun Kejariwal. 2017 · 2017
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Dominique T Shipmon, Jason M Gurevitch, Paolo M Piselli, and Stephen T Edwards. 2017 · 2017
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Carl Doersch. 2016 · 2016
Cited alongside, same era.
Less is more: Building selective anomaly ensembles
Shebuti Rayana and Leman Akoglu. 2016 · 2016
Cited alongside, same era.
Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In Proceedings of the 2018 World Wide Web Conference . 187–196
Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al · 2018
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Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 3009–3017
Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. 2019 · 2019
Later among the works it cites.