Time Series Analysis: Forecasting and Control

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John Wiley & Sons, 2015 M06 2 - 720 páginas

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.

 

Páginas seleccionadas

Contenido

Cover
Dedication
Preface to the Third Edition
Mathematical Models
3
Forecasting and Feedback Adjustment Schemes
Stochastic Models and Their Forecasting
1
Appendix A7 7 Special Note on Estimation
Model Diagnostic Checking
Table A5 3
Analysis of Seasonal Time Series
10
Arima Models
Deterministic Seasonal Components
Terms

2
1
Appendix A3 1 Autocovariances Autocovariance
Linear Nonstationary Models
2
6
Appendix A4 1 Linear Difference Equations
Appendix A4 2 IMA0 1 1 Process with
Exercises
Figure A5 2
Table A5 1
1
1
Stochastic Model Building
Parameter Estimation
Table A5 2
1
1
Space Model
Appendix A7 1 Review of Normal Distribution
Appendix A7 2 Review of Linear LeastSquares
Appendix A7 4 Exact Likelihood Function for
Appendix A9 1 Autocovariances for some
Additional Topics and Extensions
Figure A15 1
1
List of Tables
Transfer Function and Multivariate Model
4
Figure A7 1
Appendix A11 1 Continuous Models with Pulsed
1
Appendix A11 2 Nonlinear Transfer Functions
Identification Fitting and Checking
Table A9 1
Transfer Function Models
Table A15 2
Using Leading Indicators
to Estimate Transfer Functions
1
Design of Discrete Control Schemes
1
Table

Otras ediciones - Ver todas

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

Términos y frases comunes

addition alternative analysis applied approach appropriate approximate ARIMA model ARMA assumed autocorrelation function autocovariance autoregressive behavior calculated called Chapter coefficients component computed conditional consider constant correlation corresponding covariance defined depend derived described determined difference difference equation discussed distribution effect equal equation equivalent error estimates example expected exponential expression Figure fitted follows forecast given Hence identified illustration indicate infinite initial input invertible lead least-squares likelihood limits linear matrix mean methods moving average needed nonstationary normal Note observations obtained operator origin output parameters particular period plot possible practice probability procedure random referred representation represented residuals response roots sample satisfy seasonal shown shows specification spectrum squares standard stationary statistic stochastic Suppose Table transfer function unit values variables variance vector weights white noise written zero

Acerca del autor (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.

Información bibliográfica

TítuloTime Series Analysis: Forecasting and Control
Wiley Series in Probability and Statistics
AutoresGeorge E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung
Edición5, reimpresa
EditorJohn Wiley & Sons, 2015
ISBN111867491X, 9781118674918
Largo720 páginas
  
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