Probabilistic Forecasting with Conformal Prediction in Python
Probabilistic Forecasting with Conformal Prediction in Python
The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting
About the Book
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.
About the Author
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