 Forecasting with exponential smoothing is a book focused on a niche topic. I picked up this book because I like Rob Hyman's other papers and books. There is also another familiar name, Keith Ord, who wrote a fantastic, practical book on business forecasting. If you are interested in this book, check out my video review. Exponential smoothing was formulated without a statistical framework. It has been a successful forecasting algorithm in practice for decades. Wife-less about the lack of statistical framework in the backing. The authors expound on the utility of having a statistical framework for exponential smoothing. I think that if you are interested in writing forecasting software, teaching exponential smoothing, or getting a deeper understanding of forecasting methodology, this is the book for you. But I think there is more to this book than just being a passive observer of the unfolding of a statistical framework. I think this book is an example of how good forecasters think critically. If you pay attention, you may walk away with some thoughts on how to add statistical frameworks to other algorithms. One thing I appreciated was the recursive calculation tables. It was nice to be able to compare and contrast models with different trend and seasonality parameters and how to factor in errors that were additive or multiplicative. Then I could compare state space tables against the recursive formulas to see what the authors were bringing to the table. I like this section on MACE, which is mean absolute-scaled error. It is clear that this is an important metric for modern forecasting. Sections end with exercises. When you set exponential smoothing on a statistical framework, you get prediction distributions for free. Authors dedicate a full chapter on how these prediction distributions are generated. One topic that is sometimes glossed over is how to handle time series with multiple seasonal patterns. They offer some advice for practitioners on how to approach this problem within the scope of the exponential smoothing. I really enjoyed their section on count data. It is a common problem in firms, but rarely addressed in books and literature. One thing I really appreciated was the table they had comparing forecast performance of various models. I learned how to compare models a little better from this table alone. First, they used MACE as a metric. Second, they compared many models. Third, they reported the mean, median, and standard deviation. Fourth, they had a benchmark performance of a model they called Z. Z was simply predicting zero, always. Those performance was not good. It was nice to have a sense of what kind of error a simple assumption like that would work out to be. Part four is the section I have revisited the most. It is hard for practitioners to really know what other forecasters are doing in other companies. Most work is considered IP. That makes sense, and I also respect that as well. Because ideas are not always shared completely across firms, the applications sections and books are sometimes the best tutor you can have for real business scenarios. Thanks for watching, and we'll see you next time.