Time Series Analysis Forecasting and Control

ISBN-10: 0470272848
ISBN-13: 9780470272848
Edition: 4th 2008
List price: $176.00 Buy it from $71.27
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Description: This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It  More...

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Book details

List price: $176.00
Edition: 4th
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/30/2008
Binding: Hardcover
Pages: 784
Size: 6.75" wide x 9.50" long x 1.50" tall
Weight: 2.926
Language: English

This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application -forecasting, model specification, estimation, modeling the effects of intervention events, and process control, among others. In addition to meticulous modifications in content and improvements in style, the new edition 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.

GEORGE E. P. BOX, PhD, DSc, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin-Madison. He is a Fellow of the Royal Society, an Honorary Fellow and Shewhart and Deming Medalist of the American Society for Quality and was awarded the Guy Medal in Gold of the Royal Statistical Society. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association.J. STUART HUNTER, PhD, DSc, is Professor Emeritus of Civil Engineering at Princeton University. Dr. Hunter is a member of the National Academy of Engineering and has served as consultant to many industries and government agencies. He has been a staff member of the National Academy of Sciences, Committee on National Statistics; statistician in residence at the University of Wisconsin; and is the founding editor of Technometrics.The late WILLIAM G. HUNTER, PhD, was Professor of Statistics and Engineering at the University of Wisconsin-Madison.

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.

Preface to the Fourth Edition
Preface to the Third Edition
Introduction
Five Important Practical Problems
Stochastic and Deterministic Dynamic Mathematical Models
Basic Ideas in Model Building
Stochastic Models and Their Forecasting
Autocorrelation Function and Spectrum of Stationary Processes
Autocorrelation Properties of Stationary Models
Spectral Properties of Stationary Models
Link between the Sample Spectrum and Autocovariance Function Estimate
Linear Stationary Models
General Linear Process
Autoregressive Processes
Moving Average Processes
Mixed Autoregressive-Moving Average Processes
Autocovariances, Autocovariance Generating Function, and Stationarity Conditions for a General Linear Process
Recursive Method for Calculating Estimates of Autoregressive Parameters
Linear Nonstationary Models
Autoregressive Integrated Moving Average Processes
Three Explicit Forms for The Autoregressive Integrated Moving Average Model
Integrated Moving Average Processes
Linear Difference Equations
IMA(0, 1, 1) Process with Deterministic Drift
Arima Processes with Added Noise
Forecasting
Minimum Mean Square Error Forecasts and Their Properties
Calculating and Updating Forecasts
Forecast Function and Forecast Weights
Examples of Forecast Functions and Their Updating
Use of State-Space Model Formulation for Exact Forecasting
Summary
Correlations Between Forecast Errors
Forecast Weights for Any Lead Time
Forecasting in Terms of the General Integrated Form
Stochastic Model Building
Model Identification
Objectives of Identification
Identification Techniques
Initial Estimates for the Parameters
Model Multiplicity
Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process
General Method for Obtaining Initial Estimates of the Parameters of a Mixed Autoregressive-Moving Average Process
Model Estimation
Study of the Likelihood and Sum-of-Squares Functions
Nonlinear Estimation
Some Estimation Results for Specific Models
Likelihood Function Based on the State-Space Model
Unit Roots in Arima Models
Estimation Using Bayes's Theorem
Review of Normal Distribution Theory
Review of Linear Least Squares Theory
Exact Likelihood Function for Moving Average and Mixed Processes
Exact Likelihood Function for an Autoregressive Process
Asymptotic Distribution of Estimators for Autoregressive Models
Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts
Special Note on Estimation of Moving Average Parameters
Model Diagnostic Checking
Checking the Stochastic Model
Diagnostic Checks Applied to Residuals
Use of Residuals to Modify the Model
Seasonal Models
Parsimonious Models for Seasonal Time Series
Representation of the Airline Data by a Multiplicative (0, 1, 1) x (0, 1, 1)[subscript 12] Model
Some Aspects of More General Seasonal ARIMA Models
Structural Component Models and Deterministic Seasonal Components
Regression Models with Time Series Error Terms
Autocovariances for Some Seasonal Models
Nonlinear and Long Memory Models
Autoregressive Conditional Heteroscedastic (ARCH) Models
Nonlinear Time Series Models
Long Memory Time Series Processes
Transfer Function and Multivariate Model Building
Transfer Function Models
Linear Transfer Function Models
Discrete Dynamic Models Represented by Difference Equations
Relation Between Discrete and Continuous Models
Continuous Models with Pulsed Inputs
Nonlinear Transfer Functions and Linearization
Identification, Fitting, and Checking of Transfer Function Models
Cross-Correlation Function
Identification of Transfer Function Models
Fitting and Checking Transfer Function Models
Some Examples of Fitting and Checking Transfer Function Models
Forecasting With Transfer Function Models Using Leading Indicators
Some Aspects of the Design of Experiments to Estimate Transfer Functions
Use of Cross Spectral Analysis for Transfer Function Model Identification
Choice of Input to Provide Optimal Parameter Estimates
Intervention Analysis Models and Outlier Detection
Intervention Analysis Methods
Outlier Analysis for Time Series
Estimation for ARMA Models with Missing Values
Multivariate Time Series Analysis
Stationary Multivariate Time Series
Linear Model Representations for Stationary Multivariate Processes
Nonstationary Vector Autoregressive-Moving Average Models
Forecasting for Vector Autoregressive-Moving Average Processes
State-Space Form of the Vector ARMA Model
Statistical Analysis of Vector ARMA Models
Example of Vector ARMA Modeling
Design of Discrete Control Schemes
Aspects of Process Control
Process Monitoring and Process Adjustment
Process Adjustment Using Feedback Control
Excessive Adjustment Sometimes Required by MMSE Control
Minimum Cost Control with Fixed Costs of Adjustment and Monitoring
Feedforward Control
Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes
Feedback Control Schemes Where the Adjustment Variance is Restricted
Choice of the Sampling Interval
Charts and Tables
Collection of Tables and Charts
Collection of Time Series Used for Examples in the Text and in Exercises
References
Exercises and Problems
Index

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