 Business forecasting is a thick book. This makes sense because it is trying to cover all the theory and application that falls within scope of the practice of business forecasting. I had one professor tell me that books with fewer pages are harder because they expect the reader to extrapolate from theorems and lemmas. Books with more pages hold the reader's hand a little bit more. This is the case with business forecasting. It is a thick book because it spells forecasting out for the reader. I loved this. This was the first book I read which explained the Arima model in a way that made sense to me. I think the reason for this is the tables it uses. Tables are useful for people who are still getting used to reading formulas like I was years ago. Trying to comprehend new formulas is difficult. This book breaks up some calculations as one would in an Excel spreadsheet. It allows you to follow the calculations. I like that this book talks about practical and political issues surrounding forecasting in a company. This is a perspective that is not normally covered in forecasting books. For example, I learned about ABC, XYZ analysis in this book. Finally, I like how much time is dedicated to linear regression and multiple regression. Most machine learning methods require the same data setup that regression-based time series methods require. Having a good understanding of regression pays dividends. The structure of the book makes sense. At the end of the chapters, there are summary sections and problems for the reader to try. It is always a good idea to pick some random problems and give them a try. Thanks for watching.