Prediction Intervals

35 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.

Greatest papers with code

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).

Density Estimation Prediction Intervals

Curating a COVID-19 data repository and forecasting county-level death counts in the United States

Yu-Group/covid19-severity-prediction 16 May 2020

We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.

COVID-19 Tracking Decision Making +2

XGBoostLSS -- An extension of XGBoost to probabilistic forecasting

StatMixedML/XGBoostLSS 6 Jul 2019

We propose a new framework of XGBoost that predicts the entire conditional distribution of a univariate response variable.

Prediction Intervals

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals

IBM/UQ360 1 Jun 2021

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI.

Prediction Intervals

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

IBM/UQ360 9 Sep 2019

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties.

Object Localization Prediction Intervals +2

CatBoostLSS -- An extension of CatBoost to probabilistic forecasting

StatMixedML/CatBoostLSS 4 Jan 2020

We propose a new framework of CatBoost that predicts the entire conditional distribution of a univariate response variable.

Prediction Intervals

Distribution-Free Predictive Inference For Regression

ryantibs/conformal 14 Apr 2016

In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.

Prediction Intervals

With Malice Towards None: Assessing Uncertainty via Equalized Coverage

yromano/cqr 15 Aug 2019

An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers.

Prediction Intervals Recommendation Systems

Conformalized Quantile Regression

yromano/cqr NeurIPS 2019

Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.

Prediction Intervals

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

TeaPearce/Deep_Learning_Prediction_Intervals ICML 2018

This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks.

Prediction Intervals