Conformal Predictions for Longitudinal Data

• URL: http://arxiv.org/abs/2310.02863v1
• Date: Wed, 4 Oct 2023 14:51:07 GMT
• Title: Conformal Predictions for Longitudinal Data
• Abstract summary: We introduce Longitudinal Predictive Conformal Inference (L PCI), a distribution-free conformal prediction algorithm for longitudinal data. Our experiments demonstrate that LPCI outperforms existing benchmarks in terms of longitudinal coverage rates. The robust performance of LPCI in generating reliable prediction intervals for longitudinal data underscores its potential for broad applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Longitudinal Predictive Conformal Inference (LPCI), a novel distribution-free conformal prediction algorithm for longitudinal data. Current conformal prediction approaches for time series data predominantly focus on the univariate setting, and thus lack cross-sectional coverage when applied individually to each time series in a longitudinal dataset. The current state-of-the-art for longitudinal data relies on creating infinitely-wide prediction intervals to guarantee both cross-sectional and asymptotic longitudinal coverage. The proposed LPCI method addresses this by ensuring that both longitudinal and cross-sectional coverages are guaranteed without resorting to infinitely wide intervals. In our approach, we model the residual data as a quantile fixed-effects regression problem, constructing prediction intervals with a trained quantile regressor. Our extensive experiments demonstrate that LPCI achieves valid cross-sectional coverage and outperforms existing benchmarks in terms of longitudinal coverage rates. Theoretically, we establish LPCI's asymptotic coverage guarantees for both dimensions, with finite-width intervals. The robust performance of LPCI in generating reliable prediction intervals for longitudinal data underscores its potential for broad applications, including in medicine, finance, and supply chain management.

Related papers

• Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction [0.0]
  We introduce Causal Longitudinal Prior-Fitted Networks (CausalLongPFN) for time-series causal inference in longitudinal treatment-response data.<n>CausalLongPFN is a prior-fitted network for time-series causal inference in longitudinal treatment-response data and zero-shot in-context counterfactual outcome prediction.<n>We evaluate the model on branchable cancer, HIV, and warfarin benchmarks with ground-truth counterfactual labels, and on factual-only rolling-origin prediction in MIMIC-III ICU trajectories.
  arXiv  Detail & Related papers  (2026-06-04T07:26:40Z)
• DISA: Offline Importance Sampling for Distribution-Matching LLM-RL [56.9445657766829]
  We introduce DISA, which moves this calibration problem outside the RL loop.<n> DISA draws proposal trajectories offline, estimates the partition function via importance sampling, and freezes the resulting partition-function estimate.<n>On two open-weight backbones across six math and three code benchmarks, DISA matches or exceeds the online-coupled distribution-matching baseline Flow.
  arXiv  Detail & Related papers  (2026-05-17T07:14:44Z)
• Conformal Prediction with Time-Series Data via Sequential Conformalized Density Regions [0.0]
  We propose a new conformal prediction method for time-series data with a guaranteed conditional coverage rate.<n>We show that the proposed method achieves the guaranteed coverage rate under certain conditions.<n> Simulations reveal that the proposed method outperforms existing methods in terms of empirical coverage rates and set sizes.
  arXiv  Detail & Related papers  (2026-04-08T17:38:44Z)
• TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting [67.91113180885601]
  TimeAPN explicitly models and predicts non-stationary factors from both the time and frequency domains.<n>TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons.<n>It outperforms state-of-the-art reversible normalization methods.
  arXiv  Detail & Related papers  (2026-03-18T07:21:24Z)
• Leverage-Weighted Conformal Prediction [1.3537117504260623]
  Split conformal prediction provides distribution-free prediction intervals with finite-sample marginal coverage.<n>We propose Leverage-Weighted Conformal Prediction, which weights nonconformity scores by a function of the statistical leverage.
  arXiv  Detail & Related papers  (2026-02-13T07:49:39Z)
• Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction [50.094751096858204]
  LAIN is a plug-and-play framework that incorporates sequence length as a conditioning signal to balance long- and short-sequence modeling.<n>Our work offers a general, efficient, and deployable solution to mitigate length-induced bias in sequential recommendation.
  arXiv  Detail & Related papers  (2026-01-27T03:14:20Z)
• Conformal Prediction Algorithms for Time Series Forecasting: Methods and Benchmarking [0.0]
  Time series temporal dependencies violate the core assumption of data exchangeability.<n>This paper critically examines the main categories of algorithmic solutions designed to address this conflict.<n>We use AutoARIMA as the base forecaster on a large-scale monthly sales dataset.
  arXiv  Detail & Related papers  (2026-01-26T14:15:08Z)
• Distribution-informed Online Conformal Prediction [53.674678995825666]
  We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule.<n>COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate.<n>We prove that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.
  arXiv  Detail & Related papers  (2025-12-08T17:51:49Z)
• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We propose a novel distribution-free approach based on conformal prediction framework and quantile regression. We fill this void by introducing a novel conformal prediction method based on graph deep learning operators. Our approach provides accurate coverage and archives state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Conformal Thresholded Intervals for Efficient Regression [9.559062601251464]
  Conformal Thresholded Intervals (CTI) is a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage. CTI constructs prediction sets by thresholding the estimated conditional interquantile intervals based on their length.
  arXiv  Detail & Related papers  (2024-07-19T17:47:08Z)
• Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights [54.65531750162626]
  Long-term treatment effect estimation is a significant but challenging problem in many applications.<n>Existing methods rely on ideal assumptions, such as no unobserved confounders or binary treatment, to estimate long-term average treatment effects.<n>We introduce an optimal transport weighting framework to align the long-term observational data to an auxiliary short-term experimental data.
  arXiv  Detail & Related papers  (2024-06-27T14:13:46Z)
• Bayesian Frequency Estimation Under Local Differential Privacy With an Adaptive Randomized Response Mechanism [0.4604003661048266]
  We propose AdOBEst-LDP, a new algorithm for adaptive, online Bayesian estimation of categorical distributions under local differential privacy. By adapting the subset selection process to the past privatized data via Bayesian estimation, the algorithm improves the utility of future privatized data.
  arXiv  Detail & Related papers  (2024-05-11T13:59:52Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks [0.0]
  We present a temporal postprocessing method which estimates the prediction performance of convolutional long short-term memory networks. To this end, we create temporal cell state-based input metrics per segment and investigate different models for the estimation of the predictive quality.
  arXiv  Detail & Related papers  (2023-11-13T17:11:35Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Conformal Prediction Intervals with Temporal Dependence [40.282423098764404]
  Cross-sectional prediction is common in many domains such as healthcare. We focus on the task of constructing valid prediction intervals (PIs) in time-series regression with a cross-section.
  arXiv  Detail & Related papers  (2022-05-25T17:45:04Z)
• Conformal Prediction with Temporal Quantile Adjustments [40.282423098764404]
  We develop a method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. We validate TQA's performance through extensive experimentation.
  arXiv  Detail & Related papers  (2022-05-20T03:31:03Z)
• Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition [39.38412820403623]
  We develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. We introduce transfer and meta-learning algorithms for LSTD-based prediction models.
  arXiv  Detail & Related papers  (2022-03-23T20:38:48Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)

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.