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We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees.
Regression quantiles
Roger Koenker and Gilbert Bassett · 1978
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Asymptotic calibration
Dean P. Foster and Rakesh V. Vohra · 1998
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A proof of calibration via Blackwell’s approachability theorem
Dean P Foster · 1999
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Splice-2 comparative evaluation: Electricity pricing
Michael Harries · 1999
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Machine-learning applications of algorithmic randomness
Vladimir Vovk, Alexander Gammerman, and Craig Saunders · 1999
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The theta model: A decomposition approach to forecasting
Vassilis Assimakopoulos and Konstantinos Nikolopoulos · 2000
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Quantile Regression
Roger Koenker · 2005
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Algorithmic Learning in a Random World
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Calibrated structure prediction
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Estimating uncertainty online against an adversary
Volodymyr Kuleshov and Stefano Ermon · 2017
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Daily climate time series data
Sumanth Vrao · 2017
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Attention is all you need
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Exact and robust conformal inference methods for predictive machine learning with dependent data
Victor Chernozhukov, Kaspar Wuthrich, and Zhu Yinchu · 2018
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Forecasting: Principles and Practice
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S&P 500 stock data
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Forecasting at scale
Sean J. Taylor and Benjamin Letham · 2018
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel J. Candès · 2019
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Conformal prediction under covariate shift
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, and Aaditya Ramdas · 2019
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I. Jordan · 2021
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Adaptive conformal inference under distribution shift
The United States COVID-19 Forecast Hub dataset
Estee Y. Cramer, Yuxin Huang, Yijin Wang, Evan L. Ray, Matthew Cornell, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Katie House, Dasuni Jayawardena, Abdul Hannan Kanji, Ayush Khandelwal, Khoa Le, Vidhi Mody, Vrushti Mody, Jarad Niemi, Ariane Stark, Apurv Shah, Nutcha Wattanchit, Martha W. Zorn, Nicholas G. Reich, and US COVID-19 Forecast Hub Consortium · 2022
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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, et al · 2022
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Conformal prediction under feedback covariate shift for biomolecular design
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, and Michael I. Jordan · 2022
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Conformal inference of counterfactuals and individual treatment effects
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Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev and Aaditya Ramdas · 2021
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Conformal time-series forecasting
Kamile Stankeviciute, Ahmed M. Alaa, and Mihaela van der Schaar · 2021
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Conformal prediction interval for dynamic time-series
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Shai Feldman, Liran Ringel, Stephen Bates, and Yaniv Romano · 2022
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Conformal inference for online prediction with arbitrary distribution shifts
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Darts: User-friendly modern machine learning for time series
Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, and Gaël Grosch · 2022
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Adaptive conformal predictions for time series
Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, and Aymeric Dieuleveut · 2022
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Conformal prediction for time series with modern Hopfield networks
Andreas Auer, Martin Gauch, Daniel Klotz, and Sepp Hochreiter · 2023
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Improved online conformal prediction via strongly adaptive online learning
Aadyot Bhatnagar, Huan Wang, Caiming Xiong, and Yu Bai · 2023
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Conformalized survival analysis
Emmanuel J. Candès, Lihua Lei, and Zhimei Ren · 2023
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Online calibrated regression for adversarially robust forecasting
Volodymyr Kuleshov and Shachi Deshpande · 2023
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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Evan L. Ray, Logan C. Brooks, Jacob Bien, Matthew Biggerstaff, Nikos I. Bosse, Johannes Bracher, Estee Y. Cramer, Sebastian Funk, Aaron Gerding, Michael A. Johansson, Aaron Rumack, Yijin Wang, Martha Zorn, Ryan J. Tibshirani, and Nicholas G. Reich · 2023
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Sequential predictive conformal inference for time series
Chen Xu and Yao Xie · 2023
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