The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!" -Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates." -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

About the authors Bovas Abraham is Associate Professor in the Department of Statistics and Actuarial Science, at the University of Waterloo, Ontario, Canada. He is a member of the American Statistical Association, the American Society for Duality Control, the Canadian Statistical Association and a Fellow of the Royal Statistical Society. Dr. Abraham received his Ph.D. in statistics from the University of Wisconsin, Madison. Johannes Ledolter is an Associate Professor in bath the Deportment of Statistics and Actuarial Science, and the Department of Management Sciences at the University of Iowa. He is a member of the American Statistical Association and a Fellow of the Royal Statistical Society. Dr. Ledolter is also coauthor of Forecasting Using Leading Indicators. He received his Ph.D. in statistics from the University of Wisconsin, Madison.