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Probabilistic Forecasting with Conformal Prediction in Python (Early Access)

The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting

Confident forecasts aren’t just about accuracy — they’re about knowing when you might be wrong.

This book takes you deep into the fast-growing world of probabilistic forecasting and conformal prediction — modern tools that let you move beyond point estimates to deliver prediction intervals, risk measures, and trustworthy AI decisions.

Whether you’re a data scientist, ML engineer, finance professional, or academic researcher, you’ll learn how to:

Understand the theory behind conformal prediction and probabilistic forecasting — without unnecessary math overload.
Apply these methods in real-world projects: from demand forecasting to portfolio risk modeling.
Implement solutions in Python, step-by-step.
Build forecasting models that communicate uncertainty clearly to decision-makers.

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