Probabilistic Forecasting with Conformal Prediction in Python : A Practical Guide to Uncertainty Quantification for Machine Learning

Probabilistic Forecasting with Conformal Prediction in Python (Early Access)

A practical guide to calibrated uncertainty for forecasting, ML, and real decisions

Confident forecasts aren’t only about accuracy — they’re about knowing when you might be wrong, and communicating that uncertainty in a way decision-makers can use.

This book shows you how to build prediction intervals and predictive sets with coverage guarantees using conformal prediction, and how to integrate them cleanly into modern forecasting pipelines.

Early chapters cover a complete, implementation-ready foundation: split/inductive conformal prediction for forecasting, leakage-safe calibration, multi-horizon extensions, scale-normalised calibration across many series, tie handling for exact coverage, and one-sided intervals for asymmetric business costs — with step-by-step Python listings and a reproducibility checklist. Probabilistic_Forecasting_with_…

What you’ll be able to do

• Turn point forecasts into calibrated uncertainty (intervals/sets) that you can trust
• Apply conformal prediction on top of any forecaster (statistical, ML, or deep learning) Probabilistic_Forecasting_with_…
• Build leakage-safe calibration with rolling/expanding backtests
• Handle multi-horizon forecasting properly (horizon-specific thresholds) Probabilistic_Forecasting_with_…
• Calibrate across many series with scale normalization
• Use one-sided bounds when over/under-forecasting has asymmetric costs Probabilistic_Forecasting_with_…
• Report uncertainty rigorously using a reproducibility checklist (coverage, ACE, width, stratified diagnostics)