Misforecasting demand will cost profits and threaten reputations, market share, and even the business itself. Roland Martin and Stephan Kolassa draw on their expertise with retail companies to examine the major challenges for demand forecasting and the choices the demand forecaster must make....

StephanÕs review of this new book by Thomopoulos begins, ÒStay away from this book.Ó The reviewer goes on to explain both his reservations about its content and method as well as the features he likes. Copyright International Institute of Forecasters, 2015

Foresight has printed many articles about the calculation, interpretation, and especially the dangers of percentage error metrics, such as the MAPE. Stephan and Roland now add to the list of dangers, showing how you can be led astray if you use the MAPE to select a best forecasting method or to...

Kolassa reviews these two recent publications. Copyright International Institute of Forecasters, 2012

Neelie Kroes, while antitrust commissioner for the EU, said, “In the current economic context, all companies are looking for cost-effective IT solutions. Systems based on open-source software are increasingly emerging as viable alternatives to proprietary solutions.” Stephan Kolassa and Rob...

Stephan Kolassa and Wolfgang Schütz provide a careful look at the ratio MAD/Mean, which has been proposed as a substitute metric for the MAPE in the case of intermittent demand series. They explain how MAD/Mean can be viewed as a weighted mean of absolute percentage errors and thus as a...

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike’s Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference...

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike's Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference...

Concluding a section on How to Assess Forecastability (Foresight, Issue 13), Stephan Kolassa comments on papers by Peter Catt and John Boylan (in the same issue). Stephan contrasts Catt’s preferred metric, approximate entropy, with Boylan’s lowest achievable forecast error (lower bound),...