Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python

📘 Mastering Modern Time Series Forecasting (Early Access)

The book trusted by data science leaders in 100+ countries.
A practical, production-grade guide to building forecasting systems that actually work in the real world.

💸 Pricing

Price will increase to $80+ as content grows.

A tremendous amount of work and expertise has gone into this book. It is designed to deliver tangible improvements in forecasting accuracy, operational reliability, business ROI, and long-term career growth.

Forecasting is one of the most in-demand skills across nearly every industry today.

As the content continues to expand, early readers lock in lifetime access. If you find value in the book — or want to support its continued development — you’re welcome to contribute what you believe it’s worth ❤️

🧠 Why This Book Stands Out

🔑 Forecasting models are only ~5% of the problem

The remaining 95% is hard-earned, rarely documented knowledge:

• metrics that actually matter
• validation under real constraints
• deployment and monitoring
• failure modes and silent breakdowns

These are the things that determine whether a forecasting system succeeds or fails — and they are usually missing from courses, blog posts, and social media threads.

🔍 Foundations before sophistication

This book starts with what actually matters: evaluation, diagnostics, and decision-making discipline.

You’ll learn how to:

• properly evaluate forecasts
• detect when models are failing
• build with confidence rather than fragile assumptions

💎 Forecastability matters — and you’ll learn how to measure it

Not every time series is worth over-engineering.

You’ll learn how to assess the forecastability of a time series, helping you:

• allocate effort intelligently
• set realistic stakeholder expectations
• recognize diminishing returns before you waste time and budget

🧠 Built for understanding — not just coding

This is not a black-box cookbook.

You’ll understand why methods work, when they fail, and how to reason about model choices — not just how to run code.

💻 Clear, production-grade code

Every example is:

• transparent
• documented
• reusable
• designed for real workflows

No obfuscation. No throwaway scripts.

🔄 Continuously improved through real feedback

This is a living resource shaped by an active community of readers.

Many improvements and additions come directly from practitioner feedback. All readers receive:

• lifetime updates
• new chapters
• bonus tools

Thank you to everyone who has contributed — your insights are acknowledged and reflected in the book.

📚 Comprehensive, real-world coverage

The book spans:

• classical statistical models
• modern machine learning approaches
• deep learning, transformers, and foundational models

Every method is treated practically:

• tested in production
• or validated against strong academic benchmarks

No fluff. Only methods that hold up under pressure.

📈 Real ROI — for your company and your career

Readers report immediate improvements in:

• forecast accuracy
• interpretability
• stakeholder trust

This book helps you avoid silent failures and fragile systems — and build forecasting solutions that deliver measurable business impact.

✍️ About the Author

Written by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, machine learning researcher, and applied data scientist.

Valeriy has advised startups and large enterprises on building forecasting systems at scale. He has led successful initiatives for global organizations, winning competitive tenders against multinational consulting firms and specialized AI vendors.

His work has delivered multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries.
This book is now used in 100+ countries and has ranked #1 in Machine Learning, Forecasting, and Time Series across major platforms.

🌍 Trusted By and Taught To

Valeriy’s expertise is trusted by professionals at:

Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and others.

His frameworks are followed by researchers from institutions including:
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 candidates.

🎓 Want a Live, Interactive Learning Experience?

Pair this book with the Modern Forecasting Mastery course on Maven.

Join live cohorts, receive direct feedback, and build forecasting systems alongside peers.

Next cohort:
https://maven.com/valeriy-manokhin/modern-forecasting-mastery

📦 What You Get

📥 Instant access — start reading immediately
🔄 Lifetime updates — new chapters, fixes, and bonuses
💬 Private Discord access — discussions, materials, early releases, live events

🔓 Pro Edition Bonus Pack (Early Access)

Includes everything above, plus:

✅ Premium forecasting templates
✅ Extended, industry-specific case studies
✅ Annotated notebooks and exploratory pipelines
✅ Forecast model selection toolkit (Python)

Designed for professionals and teams who want to build and deploy faster — without guesswork.

👉 https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro

Ready to move from fragile forecasts to production-grade systems?
👉 Click Buy Now and start mastering modern forecasting.