11 chapters. 762 pages. 264 references. 119 figures. From foundations to foundation models — in one book.

This is the forecasting reference that didn’t exist until now.

Most forecasting books fall into one of two traps: either they rehash ARIMA and exponential smoothing as if deep learning never happened, or they throw transformer architectures at you with no evaluation discipline and no explanation of when classical methods still win. This book does neither.

Mastering Modern Time Series Forecasting is an end-to-end, practitioner-first guide to building forecasting systems that hold up in production. It covers the full stack — classical statistical models, feature-engineered ML, deep learning, transformers, and modern foundation time series models — with the evaluation and validation rigour that most books skip entirely.

Written by Valery Manokhin, PhD, MBA, CQF. PhD in Machine Learning from Royal Holloway, University of London, supervised by Professor Vladimir Vovk (the creator of Conformal Prediction). MSc in Computational Statistics and Machine Learning from UCL. Years of building and auditing production forecasting systems — and seeing exactly how projects fail when evaluation is wrong, assumptions are ignored, or shiny models replace engineering discipline.

Applied Conformal Prediction is a comprehensive and practical guide to one of the most powerful and rapidly evolving frameworks in machine learning: Conformal Prediction (CP).

Written by Valery Manokhin, who completed his PhD under Vladimir Vovk, the creator of Conformal Prediction, and has been one of its most prominent advocates for years, this book reflects deep expertise and commitment to the field. Manokhin's widely followed "Awesome Conformal Prediction" repository and his contributions to the global CP community have helped fuel its meteoric rise in research and industry.

Conformal Prediction is quickly becoming a must-have skill for anyone working in high-stakes, production-level AI systems. It provides rigorous, model-agnostic methods for quantifying uncertainty and constructing statistically valid prediction sets with guaranteed coverage. Unlike many traditional approaches, CP offers finite-sample guarantees without requiring unrealistic assumptions.

This book begins with the philosophical and mathematical origins of CP and walks you through its key components: exchangeability, nonconformity scores, prediction regions, inductive and adaptive variants, and beyond. It then explores cutting-edge research on:

• Classification
• Classifier calibration
• Regression
• Time Series and Forecasting (e.g., EnbPI, blockwise CP)
• Deep Learning Integration (NLP, CV, transformers)
• Weighted CP for covariate shift
• Software tools
• And much more

Whether you're a practitioner building risk-sensitive systems or a researcher exploring the limits of statistical inference, Applied Conformal Prediction is your definitive resource.

Preorder now to lock in the lowest price. You'll get early access to chapters as they’re released, and all future updates will be included. The price will increase significantly as more chapters are added.

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.