Time Series Analysis: Forecasting & Control (3rd Edition) [Hardcover]

George Box (Author), Gwilym M. Jenkins (Author), Gregory Reinsel (Author)

Book Description

ISBN-10: 0130607746 | ISBN-13: 978-0130607744 | Publication Date: February 28, 1994 | Edition: 3rd

This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. 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. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.

Editorial Reviews

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) --This text refers to the Kindle Edition edition.

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.

See all Editorial Reviews

Product Details

• Hardcover: 592 pages
• Publisher: Prentice Hall; 3rd edition (February 28, 1994)
• Language: English
• ISBN-10: 0130607746
• ISBN-13: 978-0130607744
• Product Dimensions: 9 x 6.1 x 1.1 inches
• Shipping Weight: 2 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #1,430,489 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

46 of 47 people found the following review helpful:

5.0 out of 5 stars revision of a classic on time series modeling, February 8, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Time Series Analysis: Forecasting & Control (3rd Edition) (Hardcover)

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.

30 of 30 people found the following review helpful:

5.0 out of 5 stars Mathematical, Theoretical, Practical., July 20, 1999

By A Customer

This review is from: Time Series Analysis: Forecasting & Control (3rd Edition) (Hardcover)

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.

1 of 1 people found the following review helpful:

5.0 out of 5 stars Great book, November 12, 2010

By 

MRM "M" (Mexico) - See all my reviews

This review is from: Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics) (Hardcover)

I read the first edition of this book, it is very clear and it is plenty of insights. My only concern is that the space given to non-linear time series in this version (3ed) might no be sufficient; any way, for me, it is a six star book.