7 Business Forecasting Books That Separate Experts from Amateurs

Nicolas Vandeput brings a rare blend of hands-on data science expertise and academic rigor to this deep dive into supply chain forecasting. Drawing on his experience founding forecasting platforms and teaching at CentraleSupelec, he breaks down complex statistical and machine learning models into practical, code-driven examples, including Python and Excel implementations. You'll find chapters on everything from traditional time series methods to neural networks, as well as guidance on managing forecasting processes and metrics like forecast value added. If you're involved in demand planning or supply chain analytics, this book arms you with concrete tools and frameworks to improve forecast accuracy and challenge vendor software limitations.

Nicolas Vandeput, a supply chain data scientist and educator, draws on years of consulting and teaching experience to clarify the complexities of demand forecasting. You’ll learn how to lead your team toward better forecast accuracy while reducing workload, use intelligent KPIs like forecast value added, and apply both statistical and machine learning models effectively. The book walks you through a unique five-step framework that adapts to your company’s needs, with chapters on capturing unconstrained demand and aligning forecasts with supply chain goals. If you’re involved in demand planning or supply chain management, this book guides you toward actionable improvements without overcomplicating the process.

This tailored book explores the essential concepts and techniques of business forecasting, focusing on your unique background and goals. It examines forecasting fundamentals, various modeling approaches, and how to interpret forecasts within real-world business contexts. By synthesizing expert knowledge with your specific interests, it reveals how to navigate uncertainties and make informed decisions. The content is carefully customized to match your experience level and target outcomes, offering a clear pathway through complex forecasting topics. Whether you're refining skills or building new expertise, this personalized guide enhances your understanding of forecasting principles, data analysis, and practical applications to boost your confidence and effectiveness.

Frank S. David, MD, PhD, brings his deep expertise as founder of Pharmagellan to illuminate the challenging world of biotech valuation. This guide unpacks how to build and interpret financial models for early-stage drug development, with chapters detailing key drivers like income, expenses, and valuation benchmarks. You'll grasp how to translate complex R&D data into realistic forecasts, essential if you're a biotech executive, investor, or advisor. This book benefits those who need to assess pipeline assets or confidently pitch their programs, offering vetted benchmarks and proprietary analyses from a seasoned consulting team.

The methods Galit Shmueli developed while navigating the complexities of business analytics form the backbone of this practical guide to time series forecasting. You’ll get hands-on experience with R software, learning techniques like ARIMA, neural networks, and smoothing algorithms tailored for real business challenges. The book dives into evaluating forecasting performance and linking models to tangible business goals, with case studies and exercises that make abstract concepts concrete. Whether you’re a student or a practitioner in marketing, finance, or supply chain, this book equips you with applicable skills rather than just theory, steering clear of jargon in favor of clarity and utility.

Drawing from his role as Procter & Gamble Bascom Professor and executive director at the Erdman Center, Enno Siemsen lays out the critical role of demand forecasting in business planning. You gain a clear understanding of forecasting fundamentals, including time series analysis and exponential smoothing, as well as the nuanced influence of human judgment on interpreting data and spotting errors. This book demystifies forecasting software by explaining the models behind the numbers and offers a framework for integrating forecasts into organizational decision-making. If you manage operations or strategy, you'll find practical insights on how forecasts shape everything from product portfolios to investment confidence.

This tailored book explores a personalized path to rapidly enhance your forecasting skills within a month. It covers essential forecasting principles and advanced techniques, focusing on your unique background and learning goals to make complex concepts accessible and practical. The content examines various forecasting models, uncertainty management, and data interpretation, guiding you through a focused learning journey designed to build confidence and precision. By tailoring the material to your specific interests and experience, this book reveals how to quickly apply forecasting insights in real-world scenarios, blending expert knowledge with your personal context. Its tailored approach ensures efficient learning by targeting what you need most to accelerate your forecasting capabilities.

Peter J. Brockwell and Richard A. Davis bring their deep expertise in statistics and time series analysis to this text aimed at providing a practical working knowledge of forecasting methods. You’ll explore core concepts such as ARMA and ARIMA processes, multivariate time series, and state-space models, with clear explanations supported by exercises and software tools like ITSM2000. The book’s inclusion of a chapter on Financial Time Series and introductions to Brownian motion and Itô calculus extends its reach beyond basics, making it ideal if you want to understand both theory and application in economics, engineering, or social sciences. This is a textbook for those comfortable with calculus and statistics, seeking to apply forecasting techniques with professional-level software guidance.

After years working at Google and Facebook, Greg Rafferty developed a hands-on approach to forecasting time series data that breaks down complex concepts into manageable steps. You learn to harness Prophet, an open-source tool designed to tackle seasonal effects, holidays, and outliers in your data with surprisingly little code. Chapters walk you through building models, tuning parameters like trend changepoints and seasonality, and diagnosing forecast accuracy with concrete metrics. If you handle forecasting with Python or R and want to improve your model performance without drowning in math, this book offers a clear path forward.