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Prediction Intervals

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.

Papers

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Showing 251–300 of 309 papers

TitleStatusHype
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Reliability and Sharpness in Border Crossing Traffic Interval Prediction—0
Conformal forecasting for surgical instrument trajectory—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Quantile Regression using Random Forest Proximities—0
Reliable Interval Prediction of Minimum Operating Voltage Based on On-chip Monitors via Conformalized Quantile Regression—0
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market—0
Reliable Prediction Intervals for Local Linear Regression—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Confidence intervals for class prevalences under prior probability shift—0
Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning—0
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Confidence Calibration for Systems with Cascaded Predictive Modules—0
Conformal Prediction Intervals for Markov Decision Process Trajectories—0
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Reliable Prediction Intervals with Regression Neural Networks—0
Conditional Testing based on Localized Conformal p-values—0
Conformal Predictions for Longitudinal Data—0
Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Conditional Coverage Estimation for High-quality Prediction Intervals—0
Conformal Prediction with Temporal Quantile Adjustments—0
Conformal Prediction with Upper and Lower Bound Models—0
Conformal Predictive Portfolio Selection—0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
Scalable computation of prediction intervals for neural networks via matrix sketching—0
Scalable Subsampling Inference for Deep Neural Networks—0
Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control—0
Computation of Ultra-Short-Term Prediction Intervals of the Power Prosumption in Active Distribution Networks—0
Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function—0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
Data-Driven Personalized Energy Consumption Range Estimation for Plug-in Hybrid Electric Vehicles in Urban Traffic—0
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation—0
CD-split and HPD-split: efficient conformal regions in high dimensions—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control—0
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals—0
Density-Calibrated Conformal Quantile Regression—0
SEF: A Method for Computing Prediction Intervals by Shifting the Error Function in Neural Networks—0
Development and Evaluation of Conformal Prediction Methods for QSAR—0
Diffusion-based Time Series Forecasting for Sewerage Systems—0
Discriminative Learning of Prediction Intervals—0
Disease Momentum: Estimating the Reproduction Number in the Presence of Superspreading—0
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds—0
Calibrated Reliable Regression using Maximum Mean Discrepancy—0
Distribution-Driven Disjoint Prediction Intervals for Deep Learning—0
Selecting informative conformal prediction sets with false coverage rate control—0
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