mlsanityData to 7 Oct 2026

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

arXiv 2202.08756 ↗First posted 17 Feb 2022cs.LGcited by 27

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Abstract (arXiv)

This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as generic machine learning algorithms performing quantile regression, which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on quantile regression or conformal prediction, and it provides sharper, more informative, and valid PIs.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 12 of its 41 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

12 of 41 references.

References · 12

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  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  5. 2019Conformalized Quantile Regressioncited by 620
  6. 2013Learning by Transductioncited by 120
  7. 2019A comparison of some conformal quantile regression methodscited by 76
  8. 2013How to Construct Deep Recurrent Neural Networkscited by 64
  9. 2019Probabilistic Forecasting with Temporal Convolutional Neural Networkcited by 63
  10. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
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  12. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 4; 2025: 8; 2026: 7.

27 papers.

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  1. 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 40
  2. 2024Conformal Prediction: A Data Perspectivecited by 25
  3. 2023A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unitcited by 14
  4. 2023How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?cited by 14
  5. 2025Relational Conformal Prediction for Correlated Time Seriescited by 11
  6. 2024On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity pricescited by 5
  7. 2025Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shifts with Unlabeled Datacited by 4
  8. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  9. 2023Conformal Loss-Controlling Predictioncited by 3
  10. 2023Conformal Predictions for Longitudinal Datacited by 3
  11. 2024Conformal time series decomposition with component-wise exchangeabilitycited by 3
  12. 2025Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Marketcited by 3
15 more
  1. 2025Test-time augmentation improves efficiency in conformal predictioncited by 3
  2. 2025ResCP: Reservoir Conformal Prediction for Time Series Forecastingcited by 3
  3. 2025MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Predictioncited by 2
  4. 2022A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingcited by 1
  5. 2023Probabilistic load forecasting with Reservoir Computingcited by 1
  6. 2023Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time seriescited by 1
  7. 2026Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variablescited by 1
  8. 2026FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Seriescited by 1
  9. 2024Interval Forecasts for Gas Prices in the Face of Structural Breaks -- Statistical Models vs. Neural Networkscited by 0
  10. 2025Improving Coverage in Combined Prediction Sets with Weighted p-valuescited by 0
  11. 2025The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Marketscited by 0
  12. 2026Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Bettercited by 0
  13. 2026Conformalized Super Learnercited by 0
  14. 2026Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagationcited by 0
  15. 2026Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecastingcited by 0