Paper Reference#

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Evgeny Burnaev and Vladislav Ishimtsev. Conformalized density- and distance-based anomaly detection in time-series data. CoRR, 2016. URL: http://arxiv.org/abs/1608.04585, arXiv:1608.04585.

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Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. Anomaly detection in streams with extreme value theory. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017, 1067–1075. ACM, 2017. URL: https://doi.org/10.1145/3097983.3098144, doi:10.1145/3097983.3098144.

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Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers. Robust random cut forest based anomaly detection on streams. In Maria-Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, volume 48 of JMLR Workshop and Conference Proceedings, 2712–2721. JMLR.org, 2016. URL: http://proceedings.mlr.press/v48/guha16.html.

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Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. Time-series anomaly detection service at microsoft. In Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, and George Karypis, editors, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, 3009–3017. ACM, 2019. URL: https://doi.org/10.1145/3292500.3330680, doi:10.1145/3292500.3330680.

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Wikipedia contributors. Standard score — Wikipedia, the free encyclopedia. 2022. [Online; accessed 19-June-2022]. URL: https://en.wikipedia.org/w/index.php?title=Standard_score&oldid=1086685336.

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Emaad A. Manzoor, Hemank Lamba, and Leman Akoglu. Xstream: outlier detection in feature-evolving data streams. In Yike Guo and Faisal Farooq, editors, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, 1963–1972. ACM, 2018. URL: https://doi.org/10.1145/3219819.3220107, doi:10.1145/3219819.3220107.

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Saket Sathe and Charu C. Aggarwal. Subspace outlier detection in linear time with randomized hashing. In Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, and Xindong Wu, editors, IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain, 459–468. IEEE Computer Society, 2016. URL: https://doi.org/10.1109/ICDM.2016.0057, doi:10.1109/ICDM.2016.0057.

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Swee Chuan Tan, Kai Ming Ting, and Fei Tony Liu. Fast anomaly detection for streaming data. In Toby Walsh, editor, IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, 1511–1516. IJCAI/AAAI, 2011. URL: https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-254, doi:10.5591/978-1-57735-516-8/IJCAI11-254.

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Tomás Pevný. Loda: lightweight on-line detector of anomalies. Mach. Learn., 102(2):275–304, 2016. URL: https://doi.org/10.1007/s10994-015-5521-0, doi:10.1007/s10994-015-5521-0.

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Wikipedia contributors. Precision and recall — Wikipedia, the free encyclopedia. 2022. [Online; accessed 19-June-2022]. URL: https://en.wikipedia.org/w/index.php?title=Precision_and_recall&oldid=1089762876.

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Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, and Justin Gottschlich. Precision and recall for time series. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, 1924–1934. 2018. URL: https://proceedings.neurips.cc/paper/2018/hash/8f468c873a32bb0619eaeb2050ba45d1-Abstract.html.

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Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262:134–147, 2017. URL: https://doi.org/10.1016/j.neucom.2017.04.070, doi:10.1016/j.neucom.2017.04.070.

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Ted Dunning. The t-digest: efficient estimates of distributions. Softw. Impacts, 7:100049, 2021. URL: https://doi.org/10.1016/j.simpa.2020.100049, doi:10.1016/j.simpa.2020.100049.