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.

Two Editions for Different Needs

📘 Core Edition
Perfect for learners and professionals who want the theory, examples, and key Python snippets.

💻 Pro Edition
Everything in Core plus:

• Full Jupyter notebooks
• End-to-end case studies (finance, business, and more)
• Extra chapters and advanced methods
• Ready-to-use Python code for your own projects

Release Plan & Pricing

• Early-bird pricing — the price will increase as more chapters are released.
• Final compiled edition will be available as both digital and print (Amazon paperback).

📅 Start mastering probabilistic forecasting today.
Get early access now and join a growing community of professionals building the next generation of trustworthy forecasting models.