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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 251–275 of 309 papers

TitleStatusHype
Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation—0
A framework for predicting, interpreting, and improving Learning Outcomes—0
Explainable boosted linear regression for time series forecasting—0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
Moment Multicalibration for Uncertainty Estimation—0
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
CD-split and HPD-split: efficient conformal regions in high dimensions—0
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep EnsemblesCode1
Efficient Conformal Prediction via Cascaded Inference with Expanded AdmissionCode1
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Calibrated Reliable Regression using Maximum Mean Discrepancy—0
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value PredictionCode1
Curating a COVID-19 data repository and forecasting county-level death counts in the United StatesCode1
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference—0
CatBoostLSS -- An extension of CatBoost to probabilistic forecastingCode1
Online Control of the False Coverage Rate and False Sign Rate—0
Optimal Uncertainty-guided Neural Network Training—0
Per-sample Prediction Intervals for Extreme Learning Machines—0
A Unified Framework for Random Forest Prediction Error EstimationCode0
Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference—0
Learn-By-Calibrating: Using Calibration as a Training Objective—0
A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks—0
Distributional conformal predictionCode0
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