Instead of covering the basics of statistical modeling, this chapter aims to provide practical extensions of forecasting methods, including:
We also include a pair of articles on the growing application of data-mining techniques in forecasting, techniques that help identify variables potentially beneficial to forecasting models. Better models support management decision making, and can enhance organizational performance.
This chapter concludes with discussions of worst-case scenarios, downside risk assessment, and disruptive events, all issues given new attention after the 2008 global financial meltdown. Forecasting delivers its value by improving management decision making. A critical component of an informed decision is the proper assessment of uncertainty.
This compilation does not substitute for the many excellent books and articles about statistical modeling and other methodological aspects of business forecasting. While a “standard” text has been Forecasting: Methods and Applications (3rd Edition, 1998) by Makridakis, Wheelwright, and Hyndman, there are a number of recent offerings including the online, open-access textbook, Forecasting: Principles and Practice by Hyndman and Athanasopoulos, and Principles of Business Forecasting by Ord and Fildes. In 2014, Foresight published a ...