Efficient quantification of time-series prediction error: optimal selection conformal prediction

Conference item

Uncertainty is almost ubiquitous in safety-critical autonomous systems due to dynamic environments and the integration of learning-based components. Quantifying this uncertainty–particularly for time-series predictions in multistage optimization–is essential for safe control and verification tasks. Conformal Prediction (CP) is a distribution-free uncertainty quantification tool with rigorous finite-sample guarantees, but its performance relies on the design of the nonconformity measure, which remains challenging for timeseries data. Existing methods either overfit on small datasets, or are computationally intensive on long-time-horizon problems and/or large datasets. To overcome these issues, we propose a new parameterization of the score functions and formulate an optimization program to compute the associated parameters. The optimal parameters directly lead to norm-ball regions that constitute minimal-average-radius conformal sets. We then provide a reformulation of the underlying optimization program to enable faster computation. We provide theoretical proofs on both the validity and efficiency of predictors constructed based on the proposed approach. Numerical results on various case studies demonstrate that our method outperforms stateof-the-art methods in terms of efficiency, with much lower computational requirements.

Authors: Pang, B; Margellos, K

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: IEEE
• Acceptance date: 2026-03-05
• Event title: 24th European Control Conference (ECC 2026)
• Event location: Reykjavík, Iceland
• Event website: https://ecc26.euca-ecc.org/
• Event start date: 2026-07-07
• Event end date: 2026-07-10
• Language: English