Time Series Analysis: Forecasting and Control

Front Cover
John Wiley & Sons, May 29, 2015 - Mathematics - 720 pages

Praise for the Fourth Edition

"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

Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.

Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes 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, the new edition covers modern topics with new features that include:

  • A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
  • An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
  • Numerous examples drawn from finance, economics, engineering, and other related fields
  • The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
  • Updates to literature references throughout and new end-of-chapter exercises
  • Streamlined chapter introductions and revisions that update and enhance the exposition

Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.

 

Selected pages

Contents

Forecasting and Control Chapter 1 Introduction
1
Forecasting and Control Part One Stochastic Models and Their Forecasting
19
Forecasting and Control Part Two Stochastic Model Building
177
Forecasting and Control Part Three Transfer Function and Multivariate Model Building
395
Forecasting and Control Part Four Design of Discrete Control Schemes
559
Forecasting and Control Part Five Charts and Tables
617
Forecasting and Control Collection of Tables and Charts
619
Forecasting and Control Collection of Time Series Used for Examples in the Text and in Exercises
625
Forecasting and Control References
642
Forecasting and Control Index
659
Forecasting and Control End User License Agreement
681
Copyright

Other editions - View all

Time Series Analysis: Forecasting and Control
George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel,Greta M. Ljung
Limited preview - 2015

Common terms and phrases

a₁ adjustment approximate ARIMA model ARIMA process ARMA autocorrelation function autocovariance autoregressive process behavior calculated Chapter coefficients component computed conditional consider covariance matrix cross-correlation deviation difference equation differencing discussed distribution dynamic Edition estimated autocorrelations example exponentially Figure filter first-order fitted forecast error forecast function George E. P. Box given Hence identified input invertibility k₁ Kalman filtering least-squares estimates likelihood function linear mean square error methods moving average moving average process multivariate N₁ nonlinear nonstationary observations obtained operator outliers output p₁ parameter estimates partial autocorrelation partial autocorrelation function plot procedure random recursively regression Reinsel representation residuals sample Section starting values state-space stationary process statistic stochastic sum of squares transfer function model variables variance VARMA vector Vz₁ w₁ weights white noise white noise process X₁ Y₁ Z₁ zero

About the author (2015)

The late George E. P. Box, PhD, was professor emeritus of statistics at the University of Wisconsin-Madison. He was a Fellow of the American Academy of Arts and Sciences and a recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association, the Shewhart Medal of the American Society for Quality, and the Guy Medal in Gold of the Royal Statistical Society. Dr. Box was also author of seven Wiley books.

The late Gwilym M. Jenkins, PhD, was professor of systems engineering at Lancaster University in the United Kingdom, where he was also founder and managing director of the International Systems Corporation of Lancaster. A Fellow of the Institute of Mathematical Statistics and the Institute of Statisticians, Dr. Jenkins had a prestigious career in both academia and consulting work that included positions at Imperial College London, Stanford University, Princeton University, and the University of Wisconsin-Madison. He was widely known for his work on time series analysis, most notably his groundbreaking work with Dr. Box on the Box-Jenkins models.

The late Gregory C. Reinsel, PhD, was professor and former chair of the department of Statistics at the University of Wisconsin-Madison. Dr. Reinsel's expertise was focused on time series analysis and its applications in areas as diverse as economics, ecology, engineering, and meteorology. He authored over seventy refereed articles and three books, and was a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics.

Greta M. Ljung, PhD, is a statistical consultant residing in Lexington, MA. She received her doctorate from the University of Wisconsin-Madison where she did her research in time series analysis under the direction of Professor George Box. Dr. Ljung's career includes teaching positions at Boston University and Massachusetts Institute of Technology, and a position as Principal Scientist at AIR Worldwide in Boston. Her many accomplishments include joint work with George Box on a time series goodness of fit test, which is widely applied in econometrics and other fields.

Bibliographic information

TitleTime Series Analysis: Forecasting and Control
Wiley Series in Probability and Statistics
AuthorsGeorge E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung
Edition5
PublisherJohn Wiley & Sons, 2015
ISBN1118674928, 9781118674925
Length720 pages
Subjects ›  › 

Mathematics / General
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
  
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