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- W4311231214 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." @default.
- W4311231214 created "2022-12-24" @default.
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- W4311231214 date "2023-01-01" @default.
- W4311231214 modified "2023-10-14" @default.
- W4311231214 title "Pareto Optimal Prediction Intervals with Hypernetworks" @default.
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- W4311231214 doi "https://doi.org/10.1016/j.asoc.2022.109930" @default.
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