mlsanityData to 14 Oct 2026

Conformal Prediction with Temporal Quantile Adjustments

arXiv 2205.09940 ↗First posted 20 May 2022stat.ML cs.LG stat.MEcited by 25

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Abstract (arXiv)

We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthcare. A canonical example in healthcare is predicting patient outcomes using physiological time-series data, where a population of patients composes a cross-section. Reliable PI estimators in this setting must address two distinct notions of coverage: cross-sectional coverage across a cross-sectional slice, and longitudinal coverage along the temporal dimension for each time series. Recent works have explored adapting Conformal Prediction (CP) to obtain PIs in the time series context. However, none handles both notions of coverage simultaneously. CP methods typically query a pre-specified quantile from the distribution of nonconformity scores on a calibration set. TQA adjusts the quantile to query in CP at each time t, accounting for both cross-sectional and longitudinal coverage in a theoretically-grounded manner. The post-hoc nature of TQA facilitates its use as a general wrapper around any time series regression model. We validate TQA's performance through extensive experimentation: TQA generally obtains efficient PIs and improves longitudinal coverage while preserving cross-sectional coverage.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 24 of its 62 references that are in the map. First posted on arXiv 20 May 2022.

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24 of 62 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
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  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2016Distribution-Free Predictive Inference For Regressioncited by 668
  5. 2019Conformalized Quantile Regressioncited by 629
  6. 2019Conformal Prediction Under Covariate Shiftcited by 519
  7. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  8. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
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  11. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  12. 2022Adaptive Conformal Predictions for Time Seriescited by 161
12 more
  1. 2017Multiplicative Normalizing Flows for Variational Bayesian Neural Networkscited by 157
  2. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  3. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  4. 2013A Conformal Prediction Approach to Explore Functional Datacited by 86
  5. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
  6. 2021Few-shot Conformal Prediction with Auxiliary Taskscited by 53
  7. 2021PAC Prediction Sets Under Covariate Shiftcited by 45
  8. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  9. 2017Bayesian Recurrent Neural Networkscited by 39
  10. 2021Locally Valid and Discriminative Prediction Intervals for Deep Learning Modelscited by 24
  11. 2020Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functionscited by 18
  12. 2019Concepts and Applications of Conformal Prediction in Computational Drug Discoverycited by 13

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Citing papers per year: 2022: 4; 2023: 3; 2024: 6; 2025: 8; 2026: 4.

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  1. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
  2. 2022Sequential Predictive Conformal Inference for Time Seriescited by 75
  3. 2024Online conformal prediction with decaying step sizescited by 56
  4. 2024Conformal prediction for multi-dimensional time series by ellipsoidal setscited by 37
  5. 2024Conformal Prediction: A Data Perspectivecited by 25
  6. 2022Split Localized Conformal Predictioncited by 23
  7. 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
  8. 2025RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecastingcited by 4
  9. 2026Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimizationcited by 4
  10. 2023Conformal Predictions for Longitudinal Datacited by 3
  11. 2023Sparse Deep Learning for Time Series Data: Theory and Applicationscited by 3
  12. 2024Conformal time series decomposition with component-wise exchangeabilitycited by 3
13 more
  1. 2025On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learningcited by 2
  2. 2026Retrieval-Corrected Conformal Prediction for Time Seriescited by 2
  3. 2024Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Predictioncited by 1
  4. 2025Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecastingcited by 1
  5. 2025Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bandscited by 1
  6. 2026Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibrationcited by 1
  7. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0
  8. 2024Distribution-Free Predictive Inference under Unknown Temporal Driftcited by 0
  9. 2025CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guaranteecited by 0
  10. 2025Statistical Guarantees in Synthetic Data through Conformal Adversarial Generationcited by 0
  11. 2025Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Modelcited by 0
  12. 2025Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inferencecited by 0
  13. 2026Online Conformal Prediction for Non-Exchangeable Panel Datacited by 0