Time Series Analysis: Forecasting and Control, 4th EditionISBN: 9780470272848
784 pages
June 2008

Description
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 indepth understanding of both timetested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upperundergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
Table of Contents
Preface to the Fourth Edition xxi
Preface to the Third Edition xxiii
1 Introduction 1
1.1 Five Important Practical Problems, 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models, 7
1.3 Basic Ideas in Model Building, 16
Part One Stochastic Models and Their Forecasting 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models, 21
2.2 Spectral Properties of Stationary Models, 35
3 Linear Stationary Models 47
3.1 General Linear Process, 47
3.2 Autoregressive Processes, 55
3.3 Moving Average Processes, 71
3.4 Mixed Autoregressive–Moving Average Processes, 79
4 Linear Nonstationary Models 93
4.1 Autoregressive Integrated Moving Average Processes, 93
4.2 Three Explicit Forms for The Autoregressive Integrated Moving Average Model, 103
4.3 Integrated Moving Average Processes, 114
5 Forecasting 137
5.1 Minimum Mean Square Error Forecasts and Their Properties, 137
5.2 Calculating and Updating Forecasts, 145
5.3 Forecast Function and Forecast Weights, 152
5.4 Examples of Forecast Functions and Their Updating, 157
5.5 Use of StateSpace Model Formulation for Exact Forecasting, 170
5.6 Summary, 177
Part Two Stochastic Model Building 193
6 Model Identification 195
6.1 Objectives of Identification, 195
6.2 Identification Techniques, 196
6.3 Initial Estimates for the Parameters, 213
6.4 Model Multiplicity, 221
7 Model Estimation 231
7.1 Study of the Likelihood and SumofSquares Functions, 231
7.2 Nonlinear Estimation, 255
7.3 Some Estimation Results for Specific Models, 268
7.4 Likelihood Function Based on the StateSpace Model, 275
7.5 Unit Roots in Arima Models, 280
7.6 Estimation Using Bayes’s Theorem, 287
8 Model Diagnostic Checking 333
8.1 Checking the Stochastic Model, 333
8.2 Diagnostic Checks Applied to Residuals, 335
8.3 Use of Residuals to Modify the Model, 350
9 Seasonal Models 353
9.1 Parsimonious Models for Seasonal Time Series, 353
9.2 Representation of the Airline Data by a Multiplicative (0, 1, 1) × (0, 1, 1)12 Model, 359
9.3 Some Aspects of More General Seasonal ARIMA Models, 375
9.4 Structural Component Models and Deterministic Seasonal Components, 384
9.5 Regression Models with Time Series Error Terms, 397
10 Nonlinear and Long Memory Models 413
10.1 Autoregressive Conditional Heteroscedastic (ARCH) Models, 413
10.2 Nonlinear Time Series Models, 420
10.3 Long Memory Time Series Processes, 428
Part Three Transfer Function and Multivariate Model Building 437
11 Transfer Function Models 439
11.1 Linear Transfer Function Models, 439
11.2 Discrete Dynamic Models Represented by Difference Equations, 447
11.3 Relation Between Discrete and Continuous Models, 458
12 Identification, Fitting, and Checking of Transfer Function Models 473
12.1 CrossCorrelation Function, 474
12.2 Identification of Transfer Function Models, 481
12.3 Fitting and Checking Transfer Function Models, 492
12.4 Some Examples of Fitting and Checking Transfer Function Models, 501
12.5 Forecasting With Transfer Function Models Using Leading Indicators, 509
12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions, 519
13 Intervention Analysis Models and Outlier Detection 529
13.1 Intervention Analysis Methods, 529
13.2 Outlier Analysis for Time Series, 536
13.3 Estimation for ARMA Models with Missing Values, 543
14 Multivariate Time Series Analysis 551
14.1 Stationary Multivariate Time Series, 552
14.2 Linear Model Representations for Stationary Multivariate Processes, 556
14.3 Nonstationary Vector Autoregressive–Moving Average Models, 570
14.4 Forecasting for Vector Autoregressive–Moving Average Processes, 573
14.5 StateSpace Form of the Vector ARMA Model, 575
14.6 Statistical Analysis of Vector ARMA Models, 578
14.7 Example of Vector ARMA Modeling, 588
Part Four Design of Discrete Control Schemes 597
15 Aspects of Process Control 599
15.1 Process Monitoring and Process Adjustment, 600
15.2 Process Adjustment Using Feedback Control, 604
15.3 Excessive Adjustment Sometimes Required by MMSE Control, 620
15.4 Minimum Cost Control with Fixed Costs of Adjustment and Monitoring, 623
15.5 Feedforward Control, 627
15.6 Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes, 642
Part Five Charts and Tables 659
Collection of Tables and Charts 661
Collection of Time Series Used for Examples in the Text and in Exercises 669
References 685
Part Six Exercises and Problems 701
Index 729
Author Information
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 WisconsinMadison. He was widely known for his work on time series analysis, most notably his groundbreaking work with Dr. Box on the BoxJenkins models.
The late Gregory CD. Reinsel, PHD, was professor and former chair of the department of Statistics at the University of WisconsinMadison. 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.
New to This Edition
 While preserving the general overall structure of the original book, several revisions, modifications, and omissions of text have been made to strengthen the understanding.
 The control section of the book has been reworked to reflect the increasing roles of process monitoring and process adjustment.
 New edition now includes coverage of multivariate statistics, with a new chapter devoted to multivariate time series analysis
 Incorporates several new topics in an effort to modernize the subject matter. These topics include extensive discussions of multivariate time series, smoothing, likelihood function based on the state space model, autoregressive models, structural component models and deterministic seasonal components, and nonlinear and long memory models.
The Wiley Advantage
 The book is lavishly displayed with graphics, exercise sets, and reallife examples from areas of study such as economics, business, engineering, and the natural sciences.
 Chapter appendices present more demanding material that can otherwise be skipped without loss of continuity.
 Extensive references provide ample content for further study.
 Every engineering, financial analyst, and applied statistician should have a copy of this invaluable guide.
Reviews
"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)
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