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title booktitle year volume series month publisher pdf url abstract layout issn id tex_title firstpage lastpage page order cycles bibtex_editor editor bibtex_author author date address container-title genre issued extras
Conformal time series decomposition with component-wise exchangeability
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications
2024
230
Proceedings of Machine Learning Research
0
PMLR
Conformal prediction offers a practical framework for distribution-free uncertainty quantification, providing finite-sample coverage guarantees under relatively mild assumptions on data exchangeability. However, these assumptions cease to hold for time series due to their temporally correlated nature. In this work, we present a novel use of conformal prediction for time series forecasting that incorporates time series decomposition. This approach allows us to model different temporal components individually. By applying specific conformal algorithms to each component and then merging the obtained prediction intervals, we customize our methods to account for the different exchangeability regimes underlying each component. Our decomposition-based approach is thoroughly discussed and empirically evaluated on synthetic and real-world data. We find that the method provides promising results on well-structured time series, but can be limited by factors such as the decomposition step for more complex data.
inproceedings
2640-3498
prinzhorn24a
Conformal time series decomposition with component-wise exchangeability
432
465
432-465
432
false
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars
given family
Simone
Vantini
given family
Matteo
Fontana
given family
Aldo
Solari
given family
Henrik
Boström
given family
Lars
Carlsson
Prinzhorn, Derck and Nijdam, Thijmen and Van der Linden, Putri and Timans, Alexander
given family
Derck
Prinzhorn
given family
Thijmen
Nijdam
given family prefix
Putri
Linden
Van der
given family
Alexander
Timans
2024-09-10
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications
inproceedings
date-parts
2024
9
10