It is five years since publication of our initial collection, Business Forecasting: Practical Problems and Solutions in 2015. Since that time the forecasting landscape has undergone a major transformation, and is now dominated by the explosion of interest in the role of artificial intelligence (AI) and machine learning (ML).
These five years have been a very exciting time of experimentation – applying existing AI/ML methods to time-series problems, and research into creating entirely new or hybrid methods. Research endeavors such as the M4 (2018) and M5 (2020) Forecasting Competitions provide important data to help evaluate the key questions we need to ask:
- Will AI/ML fundamentally change the way we do forecasting?
- Will AI/ML fundamentally improve our forecasting performance (both accuracy, and our understanding of forecast uncertainty)?
- Can AI/ML address the psychological and process issues that so greatly impact the real-life practice of forecasting?
This last question is not the least important. The value of better forecasting is delivered through better decision making (and the resulting better outcomes). So solving the statistical side of forecasting, alone, does not solve the business forecasting problem.
This chapter begins with three somewhat technical discussions of ML and deep learning, including neural networks, with examples of their application in online retail and energy. These three, along ...