Time Series Analysis Forecasting and Control

Name: Time Series Analysis Forecasting and Control
Price: 48.75 USD
Availability: InStock
ISBN: 9780470272848

ISBN-10: 0470272848

ISBN-13: 9780470272848

Edition: 4th 2008

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

List price: $176.00

30 day, 100% satisfaction guarantee!

Marketplace

3 new & used from $48.75

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

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 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…

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: 6.26" wide x 9.45" long x 1.55" tall
Weight: 2.992
Language: English

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…

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…



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