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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Friedman1
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Latest papers
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound.
Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
In such cases, marginally valid conformal prediction intervals may not provide valid coverage for the focal unit(s) due to selection bias.
A Data-Driven Supervised Machine Learning Approach to Estimating Global Ambient Air Pollution Concentrations With Associated Prediction Intervals
Global ambient air pollution, a transboundary challenge, is typically addressed through interventions relying on data from spatially sparse and heterogeneously placed monitoring stations.
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.
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals.
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Forecasting CPI inflation under economic policy and geo-political uncertainties
This study proposes a novel filtered ensemble wavelet neural network (FEWNet) that can produce reliable long-term forecasts for CPI inflation.
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
ndiamant/spice
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SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD).
UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation
Unco3892/UncertaintyPlayground
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This paper introduces UncertaintyPlayground, a Python library built on PyTorch and GPyTorch for uncertainty estimation in supervised learning tasks.
Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning
We also propose an "ensemble trick" whereby the risk for unsketched ridge regression can be efficiently estimated via GCV using small sketched ridge ensembles.
