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.
A common mistake in bad or misused software is choosing a forecasting model based solely on the model’s “fit to history” (often referred to as “best fit” or “pick best” functionality). The software provides (or the forecaster builds) several competing models which are then evaluated against recent history. The model
Of course, forecasting the stock market is not perfectly analogous to forecasting demand for a product. The asking price for a stock is largely "anchored" by the price of its most recent trades. While market values may appear to randomly drift up and down, or in a general direction, we generally
In this second of three flash reports from last week's Analytics2011 conference, we hear about a favorite topic of mine -- the relationship between demand volatility and forecastability. Rob Miller of Avantor Performance Materials, on Forecastability and Demand Volatility The "comet chart," illustrating the relationship between demand volatility and forecast
