# Prediction Intervals

37 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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Use these libraries to find Prediction Intervals models and implementations## Latest papers with *no code*

# Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application

For the global model regressed on the SET HASDM density database, we achieve errors of 11% on independent test data with well-calibrated uncertainty estimates.

# On the Relation between Prediction and Imputation Accuracy under Missing Covariates

In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction are used.

# Probabilistic predictions of SIS epidemics on networks based on population-level observations

To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model.

# Applying Regression Conformal Prediction with Nearest Neighbors to time series data

However, this assumption does not holdfor the time series data because there is a link among past, current, and future observations. Consequently, the challenge of applying conformal predictors to the problem of time seriesdata lies in the fact that observations of a time series are dependent and therefore do notmeet the exchangeability assumption.

# Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning

We benchmark our approach against the oft-preferred well-established statistical models.

# PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks

First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs.

# Efficient and Differentiable Conformal Prediction with General Function Classes

Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly over existing approaches in several applications such as prediction intervals with improved length, minimum-volume prediction sets for multi-output regression, and label prediction sets for image classification.

# Distribution-Driven Disjoint Prediction Intervals for Deep Learning

To address the issue, we propose a novel method that generates a union of disjoint PIs.

# Modelling Periodic Measurement Data Having a Piecewise Polynomial Trend Using the Method of Variable Projection

This paper presents a new method for modelling periodic signals having an aperiodic trend, using the method of variable projection.

# Uncertainty Prediction for Machine Learning Models of Material Properties

While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i. e., the evaluation of the uncertainty on each prediction, are seldomly available.