Bovas Abraham & Johannes Ledolter 
Statistical Methods for Forecasting 

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‘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 Ph D
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.

Table of Content

1. Introduction and Summary.

2. The Regression Model and Its Application in Forecasting.

3. Regression and Exponential Smoothing Methods to Forecast
Nonseasonal Time Series.

4. Regression and Exponential Smoothing Methods to Forecast
Seasonal Time Series.

5. Stochastic Time Series Models.

6. Seasonal Autoregressive Integrated Moving Average Models.

7. Relationships Between Forecasts from General Exponential
Smoothing and Forecasts from Arima Time Series Models.

8. Special Topics.

References.

Exercises.

Data Appendix.

Table Appendix.

Author Index.

Subject Index.

About the author

BOVAS ABRAHAM, Ph D, is Associate Professor in the Department of
Statistics and Actuarial Science at the University of Waterloo,
Ontario, Canada. He is a Fellow of the American Statistical
Association, and a member of the Statistical Society of Canada and
the Royal Statistical Society. Dr. Abraham received his Ph D in
statistics from the University of Wisconsin-Madison.

JOHANNES LEDOLTER, Ph D, is Associate Professor in both the
Department of Statistics and Actuarial Science and the Department
of Management Sciences at the University of Iowa. He is a Fellow of
the American Statistical Association and a member of the
International Statistical Institute. Dr. Ledolter is coauthor of
Statistical Quality Control: Strategies and Tools for Continual
Improvement and Achieving Quality Through Continual Improvement,
both published by Wiley. He received his Ph D in statistics from the
University of Wisconsin-Madison.