Mastering Modern Time Series Forecasting is a practical guide to understanding and applying statistical, machine learning, deep learning, and cutting‑edge transformer & foundational models for forecasting. Built for clarity and real‑world results, it helps data scientists, analysts, and ML engineers ship models that work in production.
🧠 Why This Book Stands Out
🔑 Forecasting models are only 5% of the equation.
The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.
🔍 It starts with what actually matters: solid foundations.
Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.
💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.
🧠 Built for understanding — not just coding.
Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.
💻 Clear, transparent production grade code.
No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.
🔄 Continuously improved through real feedback.
This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.
📚 Comprehensive, real-world coverage.
From classical time series models to deep learning, transformers and foundational models, the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.
📈 Real ROI — for your company and your career.
Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.
What you'll learn
- Core time series analysis concepts and diagnostics
- Classical models: ARIMA, ETS
- Machine learning pipelines for robust accuracy
- Deep learning: high‑performance architectures including TCNs, N‑BEATS, DeepAR, TFT, and other transformer‑based models used in real forecasting systems
- Transformers & foundational models for modern forecasting
- Validation, metrics, drift monitoring, and productionization
⭐ What Readers Say
Summary of common themes from reader feedback:
- Crystal‑clear explanations that connect theory to practice
- Production focus — metrics, validation, and deployment, not just models
- Comprehensive coverage from classical methods to transformers & foundational models
- Reusable, well‑documented code for real projects
🔓 Pro Edition Bonus Pack (Early Access) 🔥🔥🔥
Includes everything above, plus:
- ✅ Premium Forecasting Templates — plug-and-play workflows
- ✅ Extended Case Studies — deep analyses across major industries
- ✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines
- ✅ Forecast Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare
Ideal for professionals and teams who want to build and deploy faster — and sidestep the guesswork.
✍️ About the Author
Written by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, experienced data scientist, and machine learning expert and researcher with publications in top peer‑reviewed machine learning journals.
🌍 Trusted By and Taught To
Also followed by academics and researchers from: University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and more. Students include VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.