Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.
My Google Alert on "forecastability" paid off with a gem this weekend, in the blog post "Forecastability and Over Fitting" by Shaun Snapp on SCM Focus. I was not previously familiar with Shaun or this site, but found a lot to like -- in content and attitude. In his post, Shaun kindly
Paul Goodwin's Hot New Research column is a must-read in each issue of Foresight. The current column, "High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?" ends with this sage advice: If the name of a method contains more words than the number of observations that
Mean Absolute Percent Error (MAPE) is the most commonly used forecasting performance metric, and for good reason, the most disparaged. When we compute the absolute percent error the usual way, as APE = | Forecast - Actual | / Actual this is undefined when Actual = 0. It can also lead to
