# 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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## Libraries

Use these libraries to find Prediction Intervals models and implementations## Most implemented papers

# Distribution-Free Predictive Inference For Regression

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

# Conformalized Quantile Regression

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

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

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

# HDI-Forest: Highest Density Interval Regression Forest

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

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

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

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

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

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.