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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 276–300 of 309 papers

TitleStatusHype
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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