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Time Series Analysis Forecasting and Control 4th Edition by George Box, Gwilym Jenkins, Gregory Reinsel 0470272848 9780470272848

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Time Series Analysis Forecasting and Control 4th Edition George E.P. Box Digital Instant Download

Author(s): George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel
ISBN(s): 9780470272848, 0470272848
Edition: 4th
File Details: PDF, 5.85 MB
Year: 2008
Language: english
SKU: EB-10203566 Category: Tags: , ,

Time Series Analysis Forecasting and Control 4th Edition by George Box, Gwilym Jenkins, Gregory Reinsel – Ebook PDF Instant Download/Delivery: 0470272848, 9780470272848

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Product details:

ISBN 10: 0470272848

ISBN 13: 9780470272848

Author: George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel

A modernized new edition of one of the most trusted books on time series analysis. 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 n 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 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 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 the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.

Time Series Analysis Forecasting and Control 4th Table of contents:

CHAPTER ONE: Introduction

1.1 FIVE IMPORTANT PRACTICAL PROBLEMS

1.2 STOCHASTIC AND DETERMINISTIC DYNAMIC MATHEMATICAL MODELS

1.3 BASIC IDEAS IN MODEL BUILDING

PART ONE: Stochastic Models and Their Forecasting

CHAPTER TWO: Autocorrelation Function and Spectrum of Stationary Processes

2.1 AUTOCORRELATION PROPERTIES OF STATIONARY MODELS

2.2 SPECTRAL PROPERTIES OF STATIONARY MODELS

APPENDIX A2.1 LINK BETWEEN THE SAMPLE SPECTRUM AND AUTOCOVARIANCE FUNCTION ESTIMATE

CHAPTER THREE: Linear Stationary Models

3.1 GENERAL LINEAR PROCESS

3.2 AUTOREGRESSIVE PROCESSES

3.3 MOVING AVERAGE PROCESSES

3.4 MIXED AUTOREGRESSIVE–MOVING AVERAGE PROCESSES

APPENDIX A3.1 AUTOCOVARIANCES, AUTOCOVARIANCE GENERATING FUNCTION, AND STATIONARITY CONDITIONS FOR A GENERAL LINEAR PROCESS

APPENDIX A3.2 RECURSIVE METHOD FOR CALCULATING ESTIMATES OF AUTOREGRESSIVE PARAMETERS

CHAPTER FOUR: Linear Nonstationary Models

4.1 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PROCESSES

4.2 THREE EXPLICIT FORMS FOR THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL

4.3 INTEGRATED MOVING AVERAGE PROCESSES

APPENDIX A4.1 LINEAR DIFFERENCE EQUATIONS

APPENDIX A4.2 IMA(0, 1, 1) PROCESS WITH DETERMINISTIC DRIFT

APPENDIX A4.3 ARIMA PROCESSES WITH ADDED NOISE

CHAPTER FIVE: Forecasting

5.1 MINIMUM MEAN SQUARE ERROR FORECASTS AND THEIR PROPERTIES

5.2 CALCULATING AND UPDATING FORECASTS

5.3 FORECAST FUNCTION AND FORECAST WEIGHTS

5.4 EXAMPLES OF FORECAST FUNCTIONS AND THEIR UPDATING

5.5 USE OF STATE-SPACE MODEL FORMULATION FOR EXACT FORECASTING

5.6 SUMMARY

APPENDIX A5.1 CORRELATIONS BETWEEN FORECAST ERRORS

APPENDIX A5.2 FORECAST WEIGHTS FOR ANY LEAD TIME

APPENDIX A5.3 FORECASTING IN TERMS OF THE GENERAL INTEGRATED FORM

PART TWO: Stochastic Model Building

CHAPTER SIX: Model Identification

6.1 OBJECTIVES OF IDENTIFICATION

6.2 IDENTIFICATION TECHNIQUES

6.3 INITIAL ESTIMATES FOR THE PARAMETERS

6.4 MODEL MULTIPLICITY

APPENDIX A6.1 EXPECTED BEHAVIOR OF THE ESTIMATED AUTOCORRELATION FUNCTION FOR A NONSTATIONARY PROCESS

APPENDIX A6.2 GENERAL METHOD FOR OBTAINING INITIAL ESTIMATES OF THE PARAMETERS OF A MIXED AUTOREGRESSIVE–MOVING AVERAGE PROCESS

CHAPTER SEVEN: Model Estimation

7.1 STUDY OF THE LIKELIHOOD AND SUM-OF-SQUARES FUNCTIONS

7.2 NONLINEAR ESTIMATION

7.3 SOME ESTIMATION RESULTS FOR SPECIFIC MODELS

7.4 LIKELIHOOD FUNCTION BASED ON THE STATE-SPACE MODEL

7.5 UNIT ROOTS IN ARIMA MODELS

7.6 ESTIMATION USING BAYES’S THEOREM

APPENDIX A7.1 REVIEW OF NORMAL DISTRIBUTION THEORY

APPENDIX A7.2 REVIEW OF LINEAR LEAST SQUARES THEORY

APPENDIX A7.3 EXACT LIKELIHOOD FUNCTION FOR MOVING AVERAGE AND MIXED PROCESSES

APPENDIX A7.4 EXACT LIKELIHOOD FUNCTION FOR AN AUTOREGRESSIVE PROCESS

APPENDIX A7.5 ASYMPTOTIC DISTRIBUTION OF ESTIMATORS FOR AUTOREGRESSIVE MODELS

APPENDIX A7.6 EXAMPLES OF THE EFFECT OF PARAMETER ESTIMATION ERRORS ON VARIANCES OF FORECAST ERRORS AND PROBABILITY LIMITS FOR FORECASTS

APPENDIX A7.7 SPECIAL NOTE ON ESTIMATION OF MOVING AVERAGE PARAMETERS

CHAPTER EIGHT: Model Diagnostic Checking

8.1 CHECKING THE STOCHASTIC MODEL

8.2 DIAGNOSTIC CHECKS APPLIED TO RESIDUALS

8.3 USE OF RESIDUALS TO MODIFY THE MODEL

CHAPTER NINE: Seasonal Models

9.1 PARSIMONIOUS MODELS FOR SEASONAL TIME SERIES

9.2 REPRESENTATION OF THE AIRLINE DATA BY A MULTIPLICATIVE (0, 1, 1) × (0, 1, 1) 12 MODEL

9.3 SOME ASPECTS OF MORE GENERAL SEASONAL ARIMA MODELS

9.4 STRUCTURAL COMPONENT MODELS AND DETERMINISTIC SEASONAL COMPONENTS

9.5 REGRESSION MODELS WITH TIME SERIES ERROR TERMS

APPENDIX A9.1 AUTOCOVARIANCES FOR SOME SEASONAL MODELS

CHAPTER TEN: Nonlinear and Long Memory Models

10.1 AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTIC (ARCH) MODELS

10.2 NONLINEAR TIME SERIES MODELS

10.3 LONG MEMORY TIME SERIES PROCESSES

PART THREE: Transfer Function and Multivariate Model Building

CHAPTER ELEVEN: Transfer Function Models

11.1 LINEAR TRANSFER FUNCTION MODELS

11.2 DISCRETE DYNAMIC MODELS REPRESENTED BY DIFFERENCE EQUATIONS

11.3 RELATION BETWEEN DISCRETE AND CONTINUOUS MODELS

APPENDIX A11.1 CONTINUOUS MODELS WITH PULSED INPUTS

APPENDIX A11.2 NONLINEAR TRANSFER FUNCTIONS AND LINEARIZATION

CHAPTER TWELVE: Identification, Fitting, and Checking of Transfer Function Models

12.1 CROSS-CORRELATION FUNCTION

12.2 IDENTIFICATION OF TRANSFER FUNCTION MODELS

12.3 FITTING AND CHECKING TRANSFER FUNCTION MODELS

12.4 SOME EXAMPLES OF FITTING AND CHECKING TRANSFER FUNCTION MODELS

12.5 FORECASTING WITH TRANSFER FUNCTION MODELS USING LEADING INDICATORS

12.6 SOME ASPECTS OF THE DESIGN OF EXPERIMENTS TO ESTIMATE TRANSFER FUNCTIONS

APPENDIX A12.1 USE OF CROSS SPECTRAL ANALYSIS FOR TRANSFER FUNCTION MODEL IDENTIFICATION

APPENDIX A12.2 CHOICE OF INPUT TO PROVIDE OPTIMAL PARAMETER ESTIMATES

CHAPTER THIRTEEN: Intervention Analysis Models and Outlier Detection

13.1 INTERVENTION ANALYSIS METHODS

13.2 OUTLIER ANALYSIS FOR TIME SERIES

13.3 ESTIMATION FOR ARMA MODELS WITH MISSING VALUES

CHAPTER FOURTEEN: Multivariate Time Series Analysis

14.1 STATIONARY MULTIVARIATE TIME SERIES

14.2 LINEAR MODEL REPRESENTATIONS FOR STATIONARY MULTIVARIATE PROCESSES

14.3 NONSTATIONARY VECTOR AUTOREGRESSIVE-MOVING AVERAGE MODELS

14.4 FORECASTING FOR VECTOR AUTOREGRESSIVE-MOVING AVERAGE PROCESSES

14.5 STATE-SPACE FORM OF THE VECTOR ARMA MODEL

14.6 STATISTICAL ANALYSIS OF VECTOR ARMA MODELS

14.7 EXAMPLE OF VECTOR ARMA MODELING

PART FOUR: Design of Discrete Control Schemes

CHAPTER FIFTEEN: Aspects of Process Control

15.1 PROCESS MONITORING AND PROCESS ADJUSTMENT

15.2 PROCESS ADJUSTMENT USING FEEDBACK CONTROL

15.3 EXCESSIVE ADJUSTMENT SOMETIMES REQUIRED BY MMSE CONTROL

15.4 MINIMUM COST CONTROL WITH FIXED COSTS OF ADJUSTMENT AND MONITORING

15.5 FEEDFORWARD CONTROL

15.6 MONITORING VALUES OF PARAMETERS OF FORECASTING AND FEEDBACK ADJUSTMENT SCHEMES

APPENDIX A15.1 FEEDBACK CONTROL SCHEMES WHERE THE ADJUSTMENT VARIANCE IS RESTRICTED

APPENDIX A15.2 CHOICE OF THE SAMPLING INTERVAL

PART FIVE: Charts and Tables

Collection of Tables and Charts

Collection of Time Series Used for Examples in the Text and in Exercises

References

PART SIX: Exercises and Problems

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