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Practical, nontechnical solutions to the problems of business forecasting. Written in a nontechnical style, this book provides practical solutions to common business forecasting problems, showing you how to think about business forecasting in the context of uncertainty, randomness and process performance; addresses the philosophical foundations of forecasting; raises awareness of fundamental issues usually overlooked in pursuit of the perfect forecast; introduces a new way to think about business forecasting, focusing on process efficiency and the elimination of worst practices; provides practical advice.

"This title provides many of the most important and though-provoking articles by the leading business forecasting practitioners and academics. It exposes the reader to many of the best minds (and most provocative ideas) in the forecasting profession, with thorough referencing to related material for further reading. It provides: - A critical look at many of the vexing problems in business forecasting, such as volatility, forecastability, performance metrics, and human interaction in the forecasting process. - Introduces emerging new approaches such as combining data mining with forecasting and aggregating/reconciling across time hierarchies. - Addresses the often overlooked topic of data preparation and data quality (part of the "pre-processing" of data prior to forecasting. - Covers the proven (yet rarely used) method of combining forecasts to improve accuracy. Contains a mix of more formal/rigorous pieces, with brief chapters (adapted from blog posts) dealing narrowly with very specific topics"--

"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"--