📘 Mastering Modern Time Series Forecasting (early access - release)

This book price will rise to $80+ as more chapters drop. Preorder now and lock in lifetime access.

The Definitive Guide to Statistical, Machine Learning & Deep Learning Models in Python

Let’s be honest — most forecasting books are either outdated, too shallow, or written by folks who’ve never actually built a real forecasting system.

If you’ve ever felt frustrated by books that skip the basics, toss in code without explaining it, or barely touch on what forecasting really involves — you’re not alone.

This is different.

Mastering Modern Time Series Forecasting is your all-in-one, no-shortcuts guide to building reliable, high-impact forecasting systems. Whether you're just getting started or looking to deepen your expertise, this book takes you from rock-solid foundations to the latest advances in forecasting — including deep learning, transformers, and FTSM (Foundational Time Series Models).

Written by a practitioner with over a decade of experience, who’s built production-grade forecasting systems for multibillion-dollar companies, this book is grounded in reality — not hype. The systems I’ve helped build have delivered multimillion-dollar business value, but I’ve also seen the other side: data science teams chasing shiny tools, only to ship systems that crash in production, fail silently, or burn through budgets without results.

This book is a response to that — combining practical Python examples, real-world case studies, and a clear path to building forecasting solutions that actually work, scale, and deliver value.

🔍 What You'll Learn

📘 Core Forecasting Foundations

Grasp what forecast accuracy really means, master model validation strategies, and sidestep common pitfalls that trip up even experienced practitioners.

📈 Classical Models, Done Right

In-depth, modern takes on ARIMA, Exponential Smoothing, and other classical statistical and econometrics models — with clarity, not complexity.

🤖 Machine Learning for Time Series

Build feature-rich forecasts using state-of-the-art ML techniques that go far beyond black-box models.

🧠 Deep Learning & Transformers

Explore powerful deep learning architectures, including Transformer-based models — all with clear, readable PyTorch code.

📊 FTSMs – Foundational Time Series Models

Explore the rise of Foundational Time Series Models (FTSMs) — large, pre-trained models designed to generalize across domains, tasks, and time horizons. Think GPT for time series.

🎯 Probabilistic & Interpretable Forecasting

Move beyond point forecasts with uncertainty quantification, conformal prediction, SHAP, attention mechanisms, and explainability tools.

📊 Real-World Case Studies

Apply what you’ve learned on practical datasets across domains like retail, energy, and finance.

🚀 MLOps & Deployment

Learn how to deploy, monitor, and scale your forecasting pipelines in the real world — without the headaches.

👥 Who It’s For

• Data Scientists & ML Engineers
• Solving real-world forecasting challenges and building production-ready systems.
• Analysts & Developers
• Looking for a practical, hands-on reference that covers both fundamentals and advanced techniques.
• Students, Educators & Researchers
• In need of a modern, curriculum-friendly resource grounded in both theory and application.
• Demand Planners & Business Strategists
• Focused on delivering real value through accurate, actionable forecasts.

🧠 Why This Book Stands Out

• 🔍 Starts with what matters — metrics and validation
• Before jumping into models, you’ll learn how to evaluate them properly so you’re building on a solid foundation.
• 🧠 Focuses on understanding, not just coding
• Learn how methods work, why they work, and when to use them — not just how to run the code.
• 💻 Fully documented, transparent code
• No black boxes. Every example is clearly explained so you can learn and adapt, not guess.
• 🔄 Updated continuously with reader feedback
• Buy once, benefit forever — you’ll get lifetime updates as the field evolves.
• 📚 Everything in one place
• From classical models to deep learning and FTSMs — no need to juggle multiple resources ever again.

📦 What You Get

• Instant download of the full book
• All code examples, datasets, and notebooks
• Free lifetime updates (including new chapters, errata fixes, and bonus content)
• Exclusive early access to upcoming bonus chapters & Q&A sessions

💸 Pricing

• This is the initial price — it will increase as more chapters, tools, and content are released.
• If you find value or want to support the project, feel free to pay what it’s worth to you ❤️

Ready to take your forecasting skills from stats to neural nets, and from theory to real-world deployment?

👉 Hit “Buy Now” and start mastering forecasting like never before.

Advanced 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, Advanced 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.