Prediction Intervals

67 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
200

Most implemented papers

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.

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.

Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate

chancejohnstone/piRF 21 Mar 2018

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting.

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

neitzal/adaptive-skip-intervals NeurIPS 2018

We introduce a method which enables a recurrent dynamics model to be temporally abstract.

HDI-Forest: Highest Density Interval Regression Forest

chancejohnstone/piRF 24 May 2019

By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems.

A Unified Framework for Random Forest Prediction Error Estimation

benjilu/forestError 16 Dec 2019

We introduce a unified framework for random forest prediction error estimation based on a novel estimator of the conditional prediction error distribution function.

Conformal prediction interval for dynamic time-series

hamrel-cxu/EnbPI 18 Oct 2020

We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.

Conformalized Survival Analysis

zhimeir/cfsurv_paper 17 Mar 2021

Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.

RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

calakus/RFpredInterval 15 Jun 2021

The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020).

Adaptive Conformal Predictions for Time Series

mzaffran/adaptiveconformalpredictionstimeseries 15 Feb 2022

While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.