Reliable uncertainty quantification is central to modern forecasting. Conformal prediction offers model-agnostic intervals with coverage guarantee under exchangeability, but existing software often targets general prediction problems or individual …
For a given point forecaster, proportional–integral–derivative (PID) calibration configurations can attain similar overall coverage yet produce different interval widths. We introduce PID-Expert, an online aggregation method for improving the …
Conformal prediction constitutes a flexible family of methods for predictive uncertainty quantification, which has recently drawn significant attention in the statistical literature because of its distribution-free, finite-sample, and model-agnostic …
We consider the problem of constructing distribution-free prediction intervals for multi-step time series forecasting, with a focus on the temporal dependencies inherent in multi-step forecast errors. We establish that the optimal h-step-ahead …