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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 281–290 of 309 papers

TitleStatusHype
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
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