Extreme Conformal Prediction: Reliable Intervals for High-Impact Events

• URL: http://arxiv.org/abs/2505.08578v1
• Date: Tue, 13 May 2025 13:54:36 GMT
• Title: Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
• Authors: Olivier C. Pasche, Henry Lam, Sebastian Engelke,
• Abstract summary: We bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals.<n>The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.
• Score: 4.900476082341052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction is a popular method to construct prediction intervals for black-box machine learning models with marginal coverage guarantees. In applications with potentially high-impact events, such as flooding or financial crises, regulators often require very high confidence for such intervals. However, if the desired level of confidence is too large relative to the amount of data used for calibration, then classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. In this paper, we propose a new method to overcome this limitation. We bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals with high-confidence coverage, which can be constructed using any black-box extreme quantile regression method. The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.

Related papers

• Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model [0.8287206589886881]
  Time series forecasting is critical for many applications, where deep learning-based point prediction models have demonstrated strong performance.<n>Existing confidence interval modeling approaches suffer from key limitations.<n>We propose a lightweight predictoral prediction method that provides valid coverage and shorter interval lengths without retraining.
  arXiv  Detail & Related papers  (2025-05-13T01:33:53Z)
• Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict [0.5312303275762104]
  We introduce bin-conditional conformal prediction (BCCP), which enhances standard conformal prediction by ensuring consistent coverage rates across user-defined subsets.<n>Compared to standard conformal prediction, BCCP offers improved local coverage, though this comes at the cost of slightly wider prediction intervals.
  arXiv  Detail & Related papers  (2024-10-18T14:41:42Z)
• Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering [55.15192437680943]
  Generative models lack rigorous statistical guarantees for their outputs.<n>We propose a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee.<n>This guarantee states that with high probability, the prediction sets contain at least one admissible (or valid) example.
  arXiv  Detail & Related papers  (2024-10-02T15:26:52Z)
• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Regression Trees for Fast and Adaptive Prediction Intervals [2.6763498831034043]
  We present a family of methods to calibrate prediction intervals for regression problems with local coverage guarantees. We create a partition by training regression trees and Random Forests on conformity scores. Our proposal is versatile, as it applies to various conformity scores and prediction settings.
  arXiv  Detail & Related papers  (2024-02-12T01:17:09Z)
• Does confidence calibration improve conformal prediction? [10.340903334800787]
  We show that current confidence calibration methods lead to larger prediction sets in adaptive conformal prediction.<n>By investigating the role of temperature value, we observe that high-confidence predictions can enhance the efficiency of adaptive conformal prediction.<n>We propose Conformal Temperature Scaling (ConfTS), a variant of temperature scaling with a novel loss function designed to enhance the efficiency of prediction sets.
  arXiv  Detail & Related papers  (2024-02-06T19:27:48Z)
• UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation [9.387706860375461]
  We introduce a novel strategy called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA) This technique excels in efficiently aggregating multiple prediction intervals while maintaining a small average width of the prediction band and ensuring coverage. It is validated through its application to synthetic data and two real-world datasets in finance and macroeconomics.
  arXiv  Detail & Related papers  (2023-06-28T20:38:37Z)
• Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction [0.0]
  split conformal prediction represents a promising avenue to obtain finite-sample guarantees under minimal distribution-free assumptions. We highlight the connection between split conformal prediction and classical tolerance predictors developed in the 1940s.
  arXiv  Detail & Related papers  (2022-10-26T14:12:24Z)
• A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting [0.0]
  This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) It enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable.
  arXiv  Detail & Related papers  (2022-07-28T16:40:26Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)
• Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [121.10450359856242]
  We develop a frequentist procedure that utilizes influence functions of a model's loss functional to construct a jackknife (or leave-one-out) estimator of predictive confidence intervals. The DJ satisfies (1) and (2), is applicable to a wide range of deep learning models, is easy to implement, and can be applied in a post-hoc fashion without interfering with model training or compromising its accuracy.
  arXiv  Detail & Related papers  (2020-06-29T13:36:52Z)
• AutoCP: Automated Pipelines for Accurate Prediction Intervals [84.16181066107984]
  This paper proposes an AutoML framework called Automatic Machine Learning for Conformal Prediction (AutoCP) Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate. We tested AutoCP on a variety of datasets and found that it significantly outperforms benchmark algorithms.
  arXiv  Detail & Related papers  (2020-06-24T23:13:11Z)

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.