Prediction Intervals

93 papers with code • 0 benchmarks • 2 datasets

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.

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2 papers
247

Most implemented papers

Distributional Gradient Boosting Machines

statmixedml/dgbm 2 Apr 2022

We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates.

Multivariate Prediction Intervals for Random Forests

CitrineInformatics/lolo 4 May 2022

Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning.

Conformal prediction set for time-series

hamrel-cxu/ensemble-regularized-adaptive-prediction-set-eraps 15 Jun 2022

When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods.

Improving Adaptive Conformal Prediction Using Self-Supervised Learning

seedatnabeel/sscp 23 Feb 2023

However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored.

Design-based conformal prediction

valeman/awesome-conformal-prediction 2 Mar 2023

Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage.

Regression Trees for Fast and Adaptive Prediction Intervals

monoxido45/locart 12 Feb 2024

Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage.

Fast Nonparametric Conditional Density Estimation

tommyod/KDEpy 20 Jun 2012

Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx).

Inference on the Sharpe ratio via the upsilon distribution

shabbychef/SharpeR 4 May 2015

The upsilon distribution, the sum of independent chi random variates and a normal, is introduced.

Model-Robust Counterfactual Prediction Method

dzachariah/counterfactual 19 May 2017

We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures.

Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

EvgeniyaMartynova/MLiP_M5 4 Oct 2017

Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures.