Time Series Analysis: Forecasting and Control [NOOK Book]

Overview

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 in the field over the past decade through applications from areas such as business, finance, and engineering.

The Fourth Edition provides a clearly written exploration ...

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Time Series Analysis: Forecasting and Control

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Overview

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 in 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 challenges 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 anddiscussion 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 an indispensible text for statistics, business, and engineering courses on time series analysis and quality control at the upper-undergraduate and graduate levels. This book is also an invaluable reference for applied statisticians, engineers, and financial analysts.

About the Author:
George E. P. Box, PhD, is Ronald Aylmer Fisher Professor Emeritus of Statistics at the University of Wisconsin-Madison. He is 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

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Editorial Reviews

From the Publisher
'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)

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Product Details

  • ISBN-13: 9781118210871
  • Publisher: Wiley
  • Publication date: 9/20/2011
  • Series: Wiley Series in Probability and Statistics , #734
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 4
  • Pages: 784
  • Sales rank: 1,156,335
  • File size: 28 MB
  • Note: This product may take a few minutes to download.

Meet the Author

George E. P. Box, PHD, is Ronald Aylmer Fisher Professor Emeritus of Statistics at the University of Wisconsin-Madison. He is 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 is the coauthor of Statistics for Experimenters: Design, Innovation, and Discovery, Second Edition; Response Surfaces, Mixtures, and Ridge Analyses, Second Edition; Evolutionary Operation: A Statistical Method for Process Improvement; Statistical Control: By Monitoring and Feedback Adjustment; and Improving Almost Anything: Ideas and Essays, Revised Edition, all published by Wiley.

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 CD. 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.

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Table of Contents

1 Introduction 1

Pt. 1 Stochastic Models and Their Forecasting 19

2 Autocorrelation Function and Spectrum of Stationary Processes 21

3 Linear Stationary Models 47

4 Linear Nonstationary Models 93

5 Forecasting 137

Pt. 2 Stochastic Model Building 193

6 Model Identification 195

7 Model Estimation 231

8 Model Diagnostic Checking 333

9 Seasonal Models 353

10 Nonlinear and Long Memory Models 413

Pt. 3 Transfer Function and Multivariate Model Building 437

11 Transfer Function Models 439

12 Identification, Fitting, and Checking of Transfer Function Models 473

13 Intervention Analysis Models and Outlier Detection 529

14 Multivariate Time Series Analysis 551

Pt. 4 Design of Discrete Control Schemes 597

15 Aspects of Process Control 599

Pt. 5 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

Pt. 6 Exercises and Problems 701

Index 729

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