Publication: Pareto optimal prediction intervals with hypernetworks
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2023-01
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Elsevier
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Abstract
As the relevance of probabilistic forecasting grows, the need of estimating multiple high-quality prediction intervals (PI) also increases. In the current state of the art, most deep neural network gradient descent-based methods take into account interval width and coverage into a single loss function, focusing on a unique nominal coverage target, and adding additional parameters to control the coverage-width trade-off. The Pareto Optimal Prediction Interval Hypernetwork (POPI-HN) approach developed in this work has been derived to treat this coverage-width trade-off as a multi-objective problem, obtaining a complete set of Pareto Optimal solutions (Pareto front). POPI-HN are able to be trained through gradient descent with no need to add extra parameters to control the width-coverage trade-off of PIs. Once the Pareto set has been obtained, users can extract the PI with the required coverage. Comparative results with recently introduced Quality-Driven loss show similar behavior in coverage while improving interval width for the majority of the studied domains, making POPI-HN a competing alternative for estimating uncertainty in regression tasks where PIs with multiple coverages are needed.
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Direct prediction intervals estimation, Hypernetworks, Multi-objective optimization, Probabilistic forecasting, Deep neural networks
Bibliographic citation
Alcántara, A., Galván, I. M., & Aler-Mur, R. (2023). Pareto optimal prediction intervals with hypernetworks. Applied Soft Computing, 133, 109930.