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We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.
Support vector method for novelty detection
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Granger causality for time-series anomaly detection
Huida Qiu, Yan Liu, Niranjan A Subrahmanya, and Weichang Li · 2012
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Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Maximilian Christ, Andreas W. Kempa-Liehr, and Michael Feindt · 2017
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Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2017
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Alessandro Achille and Stefano Soatto · 2018
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Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 2018
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Invariant representations without adversarial training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, and Aram Galstyan · 2018
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Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Controllable invariance through adversarial feature learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, and Graham Neubig · 2018
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Unsupervised scalable representation learning for multivariate time series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi · 2019
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Causal structure based root cause analysis of outliers, 2019
Dominik Janzing, Kailash Budhathoki, Lenon Minorics, and Patrick Blöbaum · 2019
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catch22: Canonical time-series characteristics
Predicting node failures in an ultra-large-scale cloud computing platform: An aiops solution
Yangguang Li, Zhen Ming (Jack) Jiang, Heng Li, Ahmed E. Hassan, Cheng He, Ruirui Huang, Zhengda Zeng, Mian Wang, and Pinan Chen · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
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Performance Diagnosis in Cloud Microservices using Deep Learning
Li Wu, Jasmin Bogatinovski, Sasho Nedelkoski, Johan Tordsson, and Odej Kao · 2020
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Learning invariant representations using inverse contrastive loss, 2021
Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, and Vikas Singh · 2021
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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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Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz, Ben D Fulcher, and Nick S Jones · 2019
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Anomaly detection from system tracing data using multimodal deep learning
Sasho Nedelkoski, Jorge Cardoso, and Odej Kao · 2019
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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
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Representation learning with contrastive predictive coding, 2019
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
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Anomaly detection at scale: The case for deep distributional time series models, 2020
Fadhel Ayed, Lorenzo Stella, Tim Januschowski, and Jan Gasthaus · 2020
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A simple framework for contrastive learning of visual representations
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Artificial intelligence for it operations (aiops) workshop white paper, 2021
Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Florian Schmidt, Thorsten Wittkopp, Soeren Becker, Jorge Cardoso, and Odej Kao · 2021
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Neural contextual anomaly detection for time series, 2021
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus · 2021
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On the nature and types of anomalies: a review of deviations in data
Ralph Foorthuis · 2021
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A survey on contrastive self-supervised learning, 2021
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, and Xifeng Yan · 2021
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Towards a rigorous evaluation of time-series anomaly detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon · 2021
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Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister · 2021
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn Keogh · 2021
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A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
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A causality mining and knowledge graph based method of root cause diagnosis for performance anomaly in cloud applications
Juan Qiu, Qingfeng Du, Kanglin Yin, Shuang-Li Zhang, and Chongshu Qian · 2076
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