Retrieval-Corrected Conformal Prediction for Time Series

• URL: http://arxiv.org/abs/2608.10553v1
• Date: Tue, 11 Aug 2026 06:43:01 GMT
• Title: Retrieval-Corrected Conformal Prediction for Time Series
• Abstract summary: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters.<n>Recent time series CP methods improve local calibration using recent, weighted, or localized residuals.<n>Local calibration can remain indirect, since broad residual weighting or additional adaptation procedures may dilute the evidence most relevant to the current prediction.<n>We propose Retrieval-Corrected Conformal Prediction (RCCP), a retrieval-augmented calibration method for time series prediction intervals.
• Score: 31.867011243141818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions. Recent time series CP methods improve local calibration using recent, weighted, or localized residuals. Yet local calibration can remain indirect, since broad residual weighting or additional adaptation procedures may dilute the evidence most relevant to the current prediction. This motivates a simple retrieval and correction strategy that selects similar past residuals as local evidence and then corrects the coverage error left by retrieval. In this paper, we propose Retrieval--Corrected Conformal Prediction (RCCP), a retrieval-augmented calibration method for time series prediction intervals. RCCP builds an asymmetric interval from retrieved one-sided residuals and calibrates its normalized retrieval error with a scalar conformal correction. Thus, retrieval provides local residual evidence, while conformal correction determines the final scale needed for coverage. We provide a coverage-gap bound based on the stability of the normalized retrieval error distribution. Across standard benchmarks and backbone forecasters, RCCP attains the target coverage in every setting and achieves the lowest Winkler scores, with fewer severe misses. RCCP also achieves low calibration and inference overhead, showing that retrieval-corrected calibration is an effective and scalable approach to uncertainty quantification in time series forecasting. Code is available at https://github.com/jinsaaang/rccp.

Related papers

• Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction [25.59471163637912]
  Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage.<n>We provide finite-sample guarantees for the realized localized set that jointly control conditional validity and oracle efficiency.<n>We analyze data-split learned scores: when the score targets a pivotal score, uniform local guarantees decompose into fixed-score calibration and uniform score-estimation errors.
  arXiv  Detail & Related papers  (2026-08-06T15:59:57Z)
• Discovery of Hidden Miscalibration Regimes [52.452902154360565]
  Models may be systematically overconfident on some kinds of inputs and underconfident on others.<n>We define the corresponding miscalibration field and propose a diagnostic framework for estimating it.<n>Our approach learns a calibration-aware representation of the input space and estimates signed local miscalibration by kernel smoothing in the learned geometry.
  arXiv  Detail & Related papers  (2026-05-13T13:07:50Z)
• RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction [11.259098206661077]
  We introduce RareCP, a regime-aware retrieval method for adaptive conformal time series prediction.<n>RareCP learns local calibration representations through a mixture of cosine-attention experts.<n>It forms a weighted conformal quantile over their signed residuals, yielding asymmetric prediction intervals.
  arXiv  Detail & Related papers  (2026-05-09T10:12:07Z)
• When Can We Reuse a Calibration Set for Multiple Conformal Predictions? [0.0]
  We show how e-conformal prediction, in conjunction with Hoeffding's inequality, can enable the repeated use of a single calibration set.<n>We train a deep neural network and utilise a calibration set to estimate a Hoeffding correction.<n>This correction allows us to apply a modified Markov's inequality, leading to the construction of prediction sets with quantifiable confidence.
  arXiv  Detail & Related papers  (2025-06-24T14:57:25Z)
• Error-quantified Conformal Inference for Time Series [55.11926160774831]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.<n>We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function.<n>ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Rethinking Early Stopping: Refine, Then Calibrate [49.966899634962374]
  We present a novel variational formulation of the calibration-refinement decomposition.<n>We provide theoretical and empirical evidence that calibration and refinement errors are not minimized simultaneously during training.
  arXiv  Detail & Related papers  (2025-01-31T15:03:54Z)
• Normalizing Flows for Conformal Regression [0.0]
  Conformal Prediction (CP) algorithms estimate the uncertainty of a prediction model by calibrating its outputs on labeled data. We present a general scheme to localize the intervals by training the calibration process. Unlike the Error Reweighting CP algorithm of Papadopoulos et al. (2008), the framework allows estimating the gap between nominal and empirical conditional validity.
  arXiv  Detail & Related papers  (2024-06-05T15:04:28Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Adaptive Conformal Regression with Jackknife+ Rescaled Scores [7.176758110912026]
  Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution. Our approach ensures formal global coverage guarantees and is supported by new theoretical results on local coverage, including an a posteriori bound on any calibration score.
  arXiv  Detail & Related papers  (2023-05-31T14:32:26Z)
• Localized Calibration: Metrics and Recalibration [133.07044916594361]
  We propose a fine-grained calibration metric that spans the gap between fully global and fully individualized calibration. We then introduce a localized recalibration method, LoRe, that improves the LCE better than existing recalibration methods.
  arXiv  Detail & Related papers  (2021-02-22T07:22:12Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.