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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
Conditional Coverage Estimation for High-quality Prediction Intervals—0
Disease Momentum: Estimating the Reproduction Number in the Presence of Superspreading—0
Learning Prediction Intervals for Model Performance—0
Forecast with Forecasts: Diversity Matters—0
Tolerance and Prediction Intervals for Non-normal Models—0
Marginalised Spectral Mixture Kernels with Nested Sampling—0
A Deep Generative Model Imitating Predictive Coding in Human Brain—0
Prediction intervals for Deep Neural Networks—0
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
CD-split and HPD-split: efficient conformal regions in high dimensions—0
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
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference—0
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
A comparison of some conformal quantile regression methodsCode0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
With Malice Towards None: Assessing Uncertainty via Equalized CoverageCode0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
Confidence intervals for class prevalences under prior probability shift—0
Adaptive, Distribution-Free Prediction Intervals for Deep Networks—0
HDI-Forest: Highest Density Interval Regression ForestCode0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Conformalized Quantile RegressionCode0
Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function—0
tspDB: Time Series Predict DB—0
Single-Model Uncertainties for Deep LearningCode0
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical ModelsCode0
Tight Prediction Intervals Using Expanded Interval Minimization—0
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power—0
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery—0
Calibrated Prediction Intervals for Neural Network RegressorsCode0
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance EstimateCode0
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled ApproachCode0
Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
Reliability and Sharpness in Border Crossing Traffic Interval Prediction—0
Discriminative Learning of Prediction Intervals—0
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