Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction

2025/11/03 by Boyu Pang, Pang, Boyu, Kostas Margellos +1
Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2511.02103

openalex publication_date 2025/11/03 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28

Abstract

Designing effective score functions in Conformal Prediction (CP) for time-series data remains challenging due to conservativeness and/or computational inefficiency. We propose Optimal Selection Conformal Prediction (OSCP), which parameterizes the score function via offset terms. To determine these parameters, we formulate a mixed-integer linear program (MILP) that minimizes an empirical proxy of the region size. We further reformulate this optimization problem into a smaller form (fewer constraints) to improve computational efficiency. We provide theoretical guarantees on both validity and CP-efficiency of OSCP. Numerical experiments demonstrate that OSCP reduces uncertainty-set size and has much lower computational requirements compared to the state-of-the-art method.

Citations

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