Prediction Intervals
93 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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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.
Multivariate Prediction Intervals for Random Forests
Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning.
Conformal prediction set for time-series
When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored.
Design-based conformal prediction
Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage.
Regression Trees for Fast and Adaptive Prediction Intervals
Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage.
Fast Nonparametric Conditional Density Estimation
Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx).
Inference on the Sharpe ratio via the upsilon distribution
The upsilon distribution, the sum of independent chi random variates and a normal, is introduced.
Model-Robust Counterfactual Prediction Method
We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures.
Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures.