{"ID":2846896,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02103","arxiv_id":"2511.02103","title":"Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction","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.","short_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 formul...","url_abs":"https://arxiv.org/abs/2511.02103","url_pdf":"https://arxiv.org/pdf/2511.02103v4","authors":"[\"Boyu Pang\",\"Kostas Margellos\"]","published":"2025-11-03T22:35:55Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}