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This comprehensive book introduces students to time series and forecasting techniques. The prerequisites are college algebra and basic statistics. It contains complete coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting.
- Sales Rank: #2910765 in Books
- Brand: Brand: Duxbury Pr
- Published on: 1993-01-07
- Original language: English
- Number of items: 1
- Dimensions: 9.75" h x 8.50" w x 1.25" l,
- Binding: Hardcover
- 848 pages
- Used Book in Good Condition
About the Author
Bruce L. Bowerman is a professor of decision sciences at Miami University in Oxford, Ohio. He received his Ph.D. in statistics from Iowa State University in 1974 and has over 32 years of experience teaching basic statistics, regression analysis, time series forecasting, and design of experiments to both undergraduate and graduate students. In 1987 Professor Bowerman received an Outstanding Teaching award from the Miami University senior class, and in 1992 he received the Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Richard T. O¿Connell, Professor Bowerman has written ten textbooks. In addition to the earlier editions of this forecasting textbook, these textbooks include BUSINESS STATISTICS IN PRACTICE and LINEAR STATISTICAL MODELS: AN APPLIED APPROACH. The first edition of FORTECASTING AND TIME SERIES earned an Outstanding Academic Book award from CHOICE magazine. Professor Bowerman has also published a number of articles in applied stochastic processes, time series forecasting, and statistical education.
M.S. Northwestern University
Most helpful customer reviews
25 of 25 people found the following review helpful.
intermediate level time series book
By Michael R. Chernick
I reviewed the third edition of this book for the American Statistician in 1994. The book covers most of the important topics for an applied course and has a reasonable list of references. There are many examples and homework exercises. Statistical software packages such as SAS and MINITAB are used throughout in example problems. The early chapters cover the basics of statistical inference and regression (Chapters 2-5). This material can be skipped in a first time series course if introductory statistics is a prerequisite.
The latter chapters cover time series regression, seasonal decomposition methods, exponential smoothing and Box-Jenkins methods. But this book does not include nonlinear time series models and it overlooks the recent and popular state space approach to time series modeling. Multivariate time series methods are also left out, though perhaps they are more appropriate for an advanced or second course in time series analysis.
The cookbook nature of the text can be found in the guidelines given for Box-Jenkins model identification. The statistical theory that the methods rely on is avoided. Although a number of important probability distributions are used with their relevant statistical tables, the underlying assumptions and distributional theory is completely avoided.
Important concepts such as the central limit theorem and the concept of a stationary stochastic process are given only very brief treatment. Other concepts are oversimplified to avoid the need for the development of any distribution theory.
This book will serve well for a course in which the student is interested in how to implement exponential smoothing and the general class of Box-Jenkins models through the use of standard statistical packages. However if the instructor wants depth of understanding the text is not adequate. Frequecy domain methods often useful in engineering applications are not even discussed.
While the book covers forecasting applications, it does not consider applications to decomposition of variance or discriminant analysis. Time series methods are also applicable in these contexts. Abraham and Ledolter (1984) "Statistical Methods for Forecasting" cover the same topics but in much greater depth. Also Janacek and Swift (1993) "Time Series: Forecasting, Simulation, Applications" is slightly more advanced and provides broader coverage. Anyone interested in the theory can consult a number of good books including the latest edition of Brockwell and Davis "Time Series: Theory and Methods". Shumway and Stoffer (2000) "Time Series Analysis and Its Applications" is up-to-date, comprehensive and has many good engineering applications.
8 of 9 people found the following review helpful.
This book is one of the best how to books for time series
By mjs51@cam.ac.uk
This book covers step by step methodology and theory for the basic time series concepts. It has worked out examples with even the most rudimentary calculations demonstrated for complex subjects like ARMA and Box-Cox decomposition. It is a good book for basic practitioners and those with a basic interest in time series analysis
1 of 1 people found the following review helpful.
Outstanding text on time series forecasting
By P. H. Sherrod
This is a very well written textbook on time series forecasting. This 725 page textbook provides thorough coverage of time series methods from elementary statistics to Box-Jenkins models and transfer functions and intervention models. It is easy to read and includes many tables of actual data which are analyzed. Highly recommended.
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