# Prediction Intervals

89 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.

# Conformal Inference for Online Prediction with Arbitrary Distribution Shifts

We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time.

# Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

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

# 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).

# Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap

In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available.

# 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.

# Distributional Gradient Boosting Machines

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.