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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 126–150 of 309 papers

TitleStatusHype
Conformal forecasting for surgical instrument trajectory—0
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power—0
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging—0
Adaptive Conformal Regression with Jackknife+ Rescaled Scores—0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers—0
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures—0
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems—0
Forecast with Forecasts: Diversity Matters—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level—0
Joint Prediction Regions for time-series models—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression—0
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?—0
Improved conformalized quantile regression—0
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent—0
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events—0
A Wireless Foundation Model for Multi-Task Prediction—0
Foundation models for time series forecasting: Application in conformal prediction—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
From Conformal Predictions to Confidence Regions—0
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
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