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.
I've always thought of TV weather forecasters as just talking heads. Sure they look pretty, waving hands in front of fancy green-screen graphics, reading poetically off the teleprompters, and standing fearlessly in the midst of the worst storm conditions. But could we expect man candy as tart as Al Roker and Willard
Where is global warming when you need it? Throughout much of the southeast, life has been at a standstill since midday yesterday, when 2" of snow and 20oF temperatures brought civilization to its knees. If your life, or at least your forecasting career, is at a similar standstill, make plans to
In this quarter's installment of the SAS/Foresight Webinar Series, Martin Joseph and Alec Finney of Rivershill Consultancy discuss "The Forecasting Mantra." Based on their article in the Winter 2009 issue of Foresight: The International Journal of Applied Forecasting, the webinar provides a template that identifies all the elements required to achieve sustained, world-class forecasting
