Editorial Review:
The Third Edition of FORECASTING AND TIME SERIES illustrates the importance of forecasting and the various statistical techniques that can be used to produce forecasts. Bruce L. Bowerman and Richard T. O'Connell clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management.
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Customer Reviews
Average Customer Rating: 
intermediate level time series book  2008-02-17
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.
Outstanding text on time series forecasting  2005-11-11
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.
covers time series methods like a cookbook 2000-12-11
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: Foirecasting, 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.
covers time series methods like a cookbook 2000-12-11
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.
This book is one of the best how to books for time series  1998-06-05
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
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