Conformal Multistep-Ahead Multivariate Time-Series Forecasting

Filip Schlembach*, Evgueni Smirnov, Irena Koprinska, U Johansson, H Bostrom, KA Nguyen, Z Luo, L Carlsson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

This paper proposes a method for conformal multistep-ahead multivariate time-series forecasting. The method minimizes the coverage loss when the data exchangeability assumption does not properly hold. This is done by weighting residual quantiles while computing prediction intervals. Preliminary experiments on real data demonstrate the method's utility.
Original languageEnglish
Title of host publicationCONFORMAL AND PROBABILISTIC PREDICTION WITH APPLICATIONS, VOL 179
EditorsU Johansson, H Bostrom, KA Nguyen, Z Luo, L Carlsson
PublisherJMLR - Journal of Machine Learning Research
Number of pages3
Volume179
Publication statusPublished - 2022
Event11th Symposium on Conformal and Probabilistic Prediction with Applications - Brighton, United Kingdom
Duration: 24 Aug 202226 Aug 2022
https://cml.rhul.ac.uk/copa2022/

Publication series

SeriesProceedings of Machine Learning Research
Volume179
ISSN2640-3498

Conference

Conference11th Symposium on Conformal and Probabilistic Prediction with Applications
Abbreviated titleCOPA 2022
Country/TerritoryUnited Kingdom
CityBrighton
Period24/08/2226/08/22
Internet address

Keywords

  • Conformal Prediction
  • Time Series Forecasting

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