mlsanityData to 14 Oct 2026

Conformal Prediction for Time-series Forecasting with Change Points

arXiv 2509.02844 ↗First posted 2 Sep 2025cs.LG cs.AIcited by 5

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Abstract (arXiv)

Conformal prediction has been explored as a general and efficient way to provide uncertainty quantification for time series. However, current methods struggle to handle time series data with change points - sudden shifts in the underlying data-generating process. In this paper, we propose a novel Conformal Prediction for Time-series with Change points (CPTC) algorithm, addressing this gap by integrating a model to predict the underlying state with online conformal prediction to model uncertainties in non-stationary time series. We prove CPTC's validity and improved adaptivity in the time series setting under minimum assumptions, and demonstrate CPTC's practical effectiveness on 6 synthetic and real-world datasets, showing improved validity and adaptivity compared to state-of-the-art baselines.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 62 references that are in the map. First posted on arXiv 2 Sep 2025.

Builds on

35 of 62 references.

References · 35

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  2. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  3. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  5. 2016Distribution-Free Predictive Inference For Regressioncited by 668
  6. 2019Conformal Prediction Under Covariate Shiftcited by 519
  7. 2018Conditional Neural Processescited by 426
  8. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  9. 2022Conformal Risk Controlcited by 299
  10. 2017Concrete Dropoutcited by 227
  11. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  12. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
23 more
  1. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 119
  2. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117
  3. 2016Composing graphical models with neural networks for structured representations and fast inferencecited by 113
  4. 2024Theoretical Foundations of Conformal Predictioncited by 101
  5. 2024Large language model validity via enhanced conformal prediction methodscited by 92
  6. 2023Conformal PID Control for Time Series Predictioncited by 88
  7. 2022Sequential Predictive Conformal Inference for Time Seriescited by 75
  8. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  9. 2024Online conformal prediction with decaying step sizescited by 56
  10. 2021Quantifying Uncertainty in Deep Spatiotemporal Forecastingcited by 48
  11. 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 42
  12. 2016Recurrent switching linear dynamical systemscited by 35
  13. 2021Retrain or not retrain: Conformal test martingales for change-point detectioncited by 32
  14. 2019Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systemscited by 26
  15. 2017Inductive Conformal Martingales for Change-Point Detectioncited by 19
  16. 2021Deep Explicit Duration Switching Models for Time Seriescited by 17
  17. 2024Localized Adaptive Risk Controlcited by 17
  18. 2022Learning Mixtures of Linear Dynamical Systemscited by 15
  19. 2025Error-quantified Conformal Inference for Time Seriescited by 12
  20. 2023Graph Switching Dynamical Systemscited by 10
  21. 2024Conformalized Adaptive Forecasting of Heterogeneous Trajectoriescited by 10
  22. 2024Multi-model Ensemble Conformal Prediction in Dynamic Environmentscited by 8
  23. 2024Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Processcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Reverso: Efficient Time Series Foundation Models for Zero-shot Forecastingcited by 13
  2. 2026Online Localized Conformal Predictioncited by 1
  3. 2026Online Conformal Prediction: Enforcing monotonicity via Online Optimizationcited by 1
  4. 2026Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecastingcited by 0
  5. 2026The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptationcited by 0