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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 141–150 of 309 papers

TitleStatusHype
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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