“Great to see Jeffrey Tackes joining forces with Manu Joseph to finally bring the book about forecasting by the people who are actually building large scale forecasting systems for real companies.”

Valeriy Manokhin, Head of Data Science, Author of Practical Guide to Applied Conformal Prediction

“Suffices to say, the first edition of this book is an absolute must-have—and now a second edition is on the way! The only thing I thought was missing from the first edition was coverage of probabilistic forecasting; well, this has now been remedied in the form of Chapter 17, which is over 60 pages long. [...] The authors discuss the latest progress in addressing the inherent exchangeability problem in time series data, and when I say latest progress, they even refer to research papers that are barely a few months old.”

Carl McBride Ellis, PhD, Predictive Analytics, Author of The Orange Book of Machine Learning

“Modern Time Series Forecasting with Python, 2nd Edition" by Manu Joseph and Jeffrey Tackes is a must-read for data scientists looking to master time series analysis. With a structured, logical progression, from classical statistical methods to machine learning, deep learning, and industrial-grade forecasting, this book fills knowledge gaps efficiently. The foreword by Christoph Bergmeir, a leading expert, reinforces its value as a go-to resource. A perfect blend of theory and practical implementation, this book is essential for anyone serious about time series forecasting.”

Luca Zavarella, Microsoft MVP for Data and AI Platform

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son

Jeff Tackes is a seasoned data scientist specializing in demand forecasting with over a decade of industry experience. Currently he is at Kraft Heinz, where he leads the research team in charge of demand forecasting. He has pioneered the development of best-in-class forecasting systems utilized by leading Fortune 500 companies. Jeff's approach combines a robust data-driven methodology with innovative strategies, enhancing forecasting models and business outcomes significantly. Leading cross-functional teams, Jeff has designed and implemented demand forecasting systems that have markedly improved forecast accuracy, inventory optimization, and customer satisfaction. His proficiency in statistical modeling, machine learning, and advanced analytics has led to the implementation of forecasting methodologies that consistently surpass industry norms. Jeff's strategic foresight and his capability to align forecasting initiatives with overarching business objectives have established him as a trusted advisor to senior executives and a prominent expert in the data science domain. Additionally, Jeff actively contributes to the open-source community, notably to PyTimeTK, where he develops tools that enhance time series analysis capabilities. He currently resides in Chicago, IL with his wife and son.