Business forecasting : the emerging role of artificial intelligence and machine learning / edited by Michael Gilliland, Len Tashman, and Udo Sglavo.

Includes index.

Michael Gilliland (Cary, NC) is Marketing Manager for SAS forecasting software, prior to which he held forecasting positions in the food, consumer electronics, and apparel industries. He is the author of several books and writes The Business Forecasting Deal blog (blogs.sas.com/content/forecasting), is Associate Editor of Foresight: The International Journal of Applied Forecasting, and in 2017 received the Lifetime Achievement Award from the Institute of Business Forecasting. He holds a BA in Philosophy from Michigan State University, and master’s degrees in Philosophy and Mathematical Sciences from Johns Hopkins University.

Udo Sglavo (Raleigh, NC) is Director of Forecasting R&D at SAS, where he heads up a team of statisticians and midtier developers working on SAS’s award-winning software for large-scale automatic forecasting. Prior to SAS, he spent more than five years providing and consuming advanced analytical content and solutions to enterprises ranging from Fortune 500 companies to Internet startups. He is a member of the practitioner advisory board of Foresight magazine (International Institute of Forecasters).
Len Tashman (Golden, CO) is Director at the Center for Business Forecasting (CBF), which offers advice and assistance on forecast model building and customized workshops for companies and organizations worldwide. Tashman is Professor Emeritus, University of Vermont, teaching MBA courses in forecasting and decision making; is a member of the Board of Directors of the International Institute of Forecasters (IIF), the world's leading clearinghouse for the publication and dissemination of research on forecasting methods and practices; is a founding and continuing editor of Foresight: The International Journal of Applied Forecasting; and is an editor at Forecast Accuracy Measurement: Pitfalls to Avoid and Practices to Adopt.

"Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term This book provides ideas from the most important and influential authors in the field of forecasting on an array of topics that are highly relevant. It provides multiple perspectives on relevant issues like monitoring forecast performance, forecasting process, communication and accountability for the forecast, the use of big data in forecasting, and the role of AI/ML in enhancing traditional time series forecasting methods. Note: Content is mostly material previously published in "practitioner" journals (Foresight and Journal of Business Forecasting), with a few articles from the academic International Journal of Forecasting. Some articles report on academic research, or include case studies, but most are thoughtful discussion of important business forecasting topics, such as the role of the sales force in forecasting, or the value of judgmental overrides to a statistical forecast, or how to determine what forecast error is "avoidable." Articles were chosen for their importance, influence, informativeness, and for being provocative -- leading the reader to new considerations and ideas"-- Provided by publisher.

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