? The book follows faithfully the style of the original edition. Theapproach is heavily motivated by real world time series, and bydeveloping a complete approach to model building, estimation, forecasting and control.? (Mathematical Reviews, 2009)
„I think the book is very valuable and useful to graduatestudents in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even thoughthe book is written in a clear, easy to follow narrative style withplenty of illustrations, one should nevertheless have a sufficientknowledge of graduate level mathematical statistics. By reading andunderstanding the book one should, in the end, feel very confidentin time series and analysis.“ (MAA Reviews, January 13,2009)
„I think the book is very valuable and useful to graduatestudents in statistics, mathematics, engineering, and thelike. Also, it could be of tremendous help topractioners. Even though the book is written in a clear, easyto follow narrative style with plenty of illustrations, one shouldnevertheless have a sufficient knowledge of graduate levelmathematical statistics. By reading and understanding thebook one should, in the end, feel very confident in time series andanalysis.“ (MAA Reviews, January 2009)
Time Series Analysis
Forecasting and Control
von George E. P. Box, Gwilym M. Jenkins und Gregory C. ReinselA modernized new edition of one of the most trusted books on timeseries analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the mostinfluential and prominent works on the subject. This new editionmaintains its balanced presentation of the tools for modeling andanalyzing time series and also introduces the latest developmentsthat have occurred n the field over the past decade throughapplications from areas such as business, finance, and engineering.
The Fourth Edition provides a clearly written explorationof the key methods for building, classifying, testing, andanalyzing stochastic models for time series as well as their use infive important areas of application: forecasting; determining thetransfer function of a system; modeling the effects of interventionevents; developing multivariate dynamic models; and designingsimple control schemes. Along with these classical uses, moderntopics are introduced through the book's new features, whichinclude:
* A new chapter on multivariate time series analysis, includinga discussion of the challenge that arise with their modeling and anoutline of the necessary analytical tools
* New coverage of forecasting in the design of feedback andfeedforward control schemes
* A new chapter on nonlinear and long memory models, whichexplores additional models for application such as heteroscedastictime series, nonlinear time series models, and models for longmemory 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 modelswith missing values
Numerous illustrations and detailed appendices supplement thebook, while extensive references and discussion questions at the endof each chapter facilitate an in-depth understanding of bothtime-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time SeriesAnalysis, Fourth Edition is the upper-undergraduate andgraduate levels. this book is also an invaluable reference forapplied statisticians, engineers, and financial analysts.
The Fourth Edition provides a clearly written explorationof the key methods for building, classifying, testing, andanalyzing stochastic models for time series as well as their use infive important areas of application: forecasting; determining thetransfer function of a system; modeling the effects of interventionevents; developing multivariate dynamic models; and designingsimple control schemes. Along with these classical uses, moderntopics are introduced through the book's new features, whichinclude:
* A new chapter on multivariate time series analysis, includinga discussion of the challenge that arise with their modeling and anoutline of the necessary analytical tools
* New coverage of forecasting in the design of feedback andfeedforward control schemes
* A new chapter on nonlinear and long memory models, whichexplores additional models for application such as heteroscedastictime series, nonlinear time series models, and models for longmemory 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 modelswith missing values
Numerous illustrations and detailed appendices supplement thebook, while extensive references and discussion questions at the endof each chapter facilitate an in-depth understanding of bothtime-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time SeriesAnalysis, Fourth Edition is the upper-undergraduate andgraduate levels. this book is also an invaluable reference forapplied statisticians, engineers, and financial analysts.