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Time Series Analysis: Forecasting and Control, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel
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A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering.
The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include:
- A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools
- New coverage of forecasting in the design of feedback and feedforward control schemes
- A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes
- Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series
- A review of the maximum likelihood estimation for ARMA models with missing values
Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
- Sales Rank: #173856 in Books
- Published on: 2008-06-30
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.58" w x 6.40" l, 2.63 pounds
- Binding: Hardcover
- 784 pages
Review
?The book follows faithfully the style of the original edition. The approach is heavily motivated by real world time series, and by developing a complete approach to model building, estimation, forecasting and control.? (Mathematical Reviews, 2009)
"I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." (MAA Reviews, January 13, 2009)
"I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like.� Also, it could be of tremendous help to practioners.� Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics.� By reading and understanding the book one should, in the end, feel very confident in time series and analysis." (MAA Reviews, January 2009)
From the Publisher
This is a complete revision of a classic, seminal, and authoritative text that has been the model for most books on the topic written since 1970. It explores the building of stochastic (statistical) models for time series and their use in important areas of application --forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
From the Back Cover
A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering.
The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include:
- A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools
- New coverage of forecasting in the design of feedback and feedforward control schemes
- A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes
- Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series
- A review of the maximum likelihood estimation for ARMA models with missing values
Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
Most helpful customer reviews
67 of 69 people found the following review helpful.
revision of a classic on time series modeling
By Michael R. Chernick
In the early 1970s I was working on practical forecasting methods to apply to the U.S. Army supply depot workloads. Exponential smoothing was the commonly used "automatic" technique (once smoothing constants have been determined) that had great advantages over the informal methods used by the Army. Then someone told me that Box-Jenkins techniques were more general and powerful. I got a copy of the first edition published in 1970 and found that I could read and understand it even though I had little statistical training. I had a bachelors degree in mathematics. I got to appreciate the book even more when I took a short course from George Box, George Tiao and David Pack based on the book. I began to grasp some of the key ideas of stationary and nonstationary time series and learned about model selection, diagnostic checking and estimation. This started my interest in becoming a statistician and gave me the practical side of time series analysis first. I later specialized in it and got a Ph.D. in statistics.
Gwilym Jenkins died many years prior to this edition and Box's colleague Greogory Reinsel took on the task of helping to revise and update it.
It retains its original flavor. It is an applied book with many practical and illustrative examples. It concentrates on the three stages of time series analysis: modeling building, selection, estimation and diagnostic checking and how to iterate the process toward a good solution. The ARIMA time series models are what are considered. The theory of stationary and nonstationary time series is introduced to motivate interpretation of autocorrelation and partial autocorrelation in the model identification phase. Operator notation is introduced and used throughout the book to simplify equations. For me it helped simplify things and illuminate some concepts. But many readers found it difficult and confusing. the book is very systematic and practical. Many of the examples are real examples from Box's work in the chemical industry and his consulting during his career at the University of Wisconsin and also the consulting experience of Gwilym Jenkins in England.
The publishers and some amazon reviewers say that this edition is a major revision. The second edition published in 1976 was criticized for being essentially a reprint of the first. Although there is a new chapter 12 on intervention analysis and outlier detection it mainly is an expansion of ideas already discussed in the first edition. Theoretical results are kept aside in appendices as in previous editions.
This is not an up-to-date text on the theory of time series. It deals strictly with the time domain approach and does not include recent advances including nonlinear and bilinear models, models with non-Gaussian innovations and bootstrap or other resampling methods.
To get a balanced approach that includes the theory for frequency and time domain approaches the book by Shumway, the latest edition of the Brockwell and Davis text and the latest edition of Fuller's text are appropriate. For a graduate course I taught at UC Santa Barbara in 1981 I used the first edition of Fuller's book. Anderson provides a thorough account of the time domain theory. Excellent texts that specialize in the frequency domain approach are Bloomfield's second edition and the two volume book by Priestley. Brillinger's text is also worthwhile for those interested in spectral theory (frequency domain statistics).
Although there are many things that is text does not cover, it remains the classical text on a rich class of time domain methods that are still very practical. This is a text I bought for reference even though I still have the first edition.
35 of 35 people found the following review helpful.
Mathematical, Theoretical, Practical.
By A Customer
Box-Jenkins is THE definitive, foundational text in time series analysis. Mastery of this volume requires extensive graduate level understanding of mathematical statistics. While difficult even for intermediate statistical practitioners, this text is necessary for any professional who examines time series data and well worth the considerable effort to acquire mastery.
27 of 28 people found the following review helpful.
Disappointed
By Samson
Based on the reviews here I had high hopes for learning the basics of ARIMA from this text. My fundamental beef with the book is not the content, but rather the presentation. I have taught portions of what's in the book for decades in the classroom on an undergraduate level. The author's writing style was such that even in those parts I could barely follow the presentation. I have bought literally hundreds of books from Amazon over the years they've been in business. This will be the first one I return. If you're trying to teach yourself this material, find a better aid. If you're buying this for a live class, don't skip class.
UPDATE: Because of the need to find useful sources for real world work, the search has continued. As a result, a very good resource has been found: Forecating Methods and Applications, Third Edn. by Makridakis. Of all the resources investigated, it presents the clearest explanations out there of the different forecasting methods, while still giving valuable insight. For a deeper look, Forecasting and Time Series: An Applied Approach by Bowerman & O'Connell has also proven useful. An older book, it is clearly written for the practitioner. And if you're using SAS to work through problems of this type: SAS/ETS Software: Applications Guide 1 is helpful.
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