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This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios.
Limiting forms of the frequency distribution of the largest or smallest member of a sample
Ronald Aylmer Fisher and Leonard Henry Caleb Tippett · 1928
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Residual life time at great age
August A Balkema and Laurens De Haan · 1974
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Statistical inference using extreme order statistics
James Pickands III et al · 1975
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The stabilized probability plot
John R Michael · 1983
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On a basis for ‘peaks over threshold’ modeling
M.R. Leadbetter · 1991
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Financial risk and heavy tails
Brendan O Bradley and Murad S Taqqu · 2003
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Statistics of extremes: theory and applications
Jan Beirlant, Yuri Goegebeur, Johan Segers, and Jozef L Teugels · 2006
Cited alongside, same era.
Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis
Nejc Bezak, Mitja Brilly, and Mojca Šraj · 2014
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Statistics of extremes
Anthony C Davison and Raphaël Huser · 2015
Cited alongside, same era.
Unsupervised real-time anomaly detection for streaming data
Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha · 2017
Cited alongside, same era.
Anomaly detection in streams with extreme value theory
Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouet · 2017
Cited alongside, same era.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
Later among the works it cites.
Lstm-based anomaly detection: Detection rules from extreme value theory
Neema Davis, Gaurav Raina, and Krishna Jagannathan · 2019
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Neural forecasting: Introduction and literature overview
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, et al · 2020
Later among the works it cites.
Heavy-tailed time series
Rafal Kulik and Philippe Soulier · 2020
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The effectiveness of discretization in forecasting: An empirical study on neural time series models
Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, and Jan Gasthaus · 2020
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