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

Includes bibliographical references and index.

Artificial intelligence and machine learning in forecasting -- Big data in forecasting -- Forecasting methods: modeling, selection, and monitoring -- Forecasting performance -- Forecasting process: communication, accountability, and S&OP -- Afterwords: essays on topics in business forecasting.

"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.