Abraham / Ledolter Statistical Methods for Forecasting

1. Auflage 2009
ISBN: 978-0-470-31729-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 472 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists.
"This book, it must be said, lives up to the words on itsadvertising cover: 'Bridging the gap between introductory,descriptive approaches and highly advanced theoretical treatises,it provides a practical, intermediate level discussion of a varietyof 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 modelsthat can be used to produce short-term forecasts, this book haswide-ranging applications. It could be used in the context of astudy of regression, forecasting, and time series analysis by PhDstudents; or to support a concentration in quantitative methods forMBA students; or as a work in applied statistics for advancedundergraduates."
-Choice
Statistical Methods for Forecasting is a comprehensive, readabletreatment of statistical methods and models used to produceshort-term forecasts. The interconnections between the forecastingmodels and methods are thoroughly explained, and the gap betweentheory and practice is successfully bridged. Special topics arediscussed, such as transfer function modeling; Kalman filtering;state space models; Bayesian forecasting; and methods for forecastevaluation, comparison, and control. The book provides time series,autocorrelation, and partial autocorrelation plots, as well asexamples and exercises using real data. Statistical Methods forForecasting serves as an outstanding textbook for advancedundergraduate and graduate courses in statistics, business,engineering, and the social sciences, as well as a workingreference for professionals in business, industry, and government.

Autoren/Hrsg.

Abraham, Bovas

Ledolter, Johannes

Inhaltsverzeichnis

1. Introduction and Summary.
2. The Regression Model and Its Application in Forecasting.
3. Regression and Exponential Smoothing Methods to ForecastNonseasonal Time Series.
4. Regression and Exponential Smoothing Methods to ForecastSeasonal Time Series.
5. Stochastic Time Series Models.
6. Seasonal Autoregressive Integrated Moving Average Models.
7. Relationships Between Forecasts from General ExponentialSmoothing and Forecasts from Arima Time Series Models.
8. Special Topics.
References.
Exercises.
Data Appendix.
Table Appendix.
Author Index.
Subject Index.

Über Autor(innen)

BOVAS ABRAHAM, PhD, is Associate Professor in the Department ofStatistics and Actuarial Science at the University of Waterloo,Ontario, Canada. He is a Fellow of the American StatisticalAssociation, and a member of the Statistical Society of Canada andthe Royal Statistical Society. Dr. Abraham received his PhD instatistics from the University of Wisconsin-Madison.
JOHANNES LEDOLTER, PhD, is Associate Professor in both theDepartment of Statistics and Actuarial Science and the Departmentof Management Sciences at the University of Iowa. He is a Fellow ofthe American Statistical Association and a member of theInternational Statistical Institute. Dr. Ledolter is coauthor ofStatistical Quality Control: Strategies and Tools for ContinualImprovement and Achieving Quality Through Continual Improvement,both published by Wiley. He received his PhD in statistics from theUniversity of Wisconsin-Madison.