Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915972951048192 |
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| author | Pang, Boyu Margellos, Kostas |
| author_facet | Pang, Boyu Margellos, Kostas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction Pang, Boyu Margellos, Kostas Optimization and Control 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. |
| title | Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.02103 |