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Time series analysis : (Record no. 9974)

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000 -LEADER
fixed length control field	07875cam a2200301 i 4500
005 - DATE & TIME
control field	20230724154747.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	150417s2016 njua b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number	2015015492
020 ## - ISBN
International Standard Book Number	9781118675021
Price	13,777.00
040 ## - CATALOGING SOURCE
Original cataloging agency	S.X.U.K
041 ## - Language
Language	English
082 00 - DDC NUMBER
Classification number	R 519.55 TIM
245 10 - TITLE STATEMENT
Title	Time series analysis :
Sub Title	forecasting and control.
Statement of responsibility	George E.P. Box, Gwilym M. Jenkins, Georgory C. Reinsel, Greta M. Ljung
250 ## - EDITION STATEMENT
Edition statement	5th ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	New Jersey
Name of publisher, distributor, etc	John Wiley
Date of publication, distribution, etc	2016
300 ## - PHYSICAL DESCRIPTION
Pages	xxvi, 669 pages :
Other Details	illustrations ;
Dimension	26 cm.H.B.
440 ## - Series Statement
Series Title	Wiley Series in Probability and Statistics
500 ## - GENERAL NOTE
General note	TABLE OF CONTENTS<br/>PREFACE TO THE FIFTH EDITION xix<br/><br/>PREFACE TO THE FOURTH EDITION xxiii<br/><br/>PREFACE TO THE THIRD EDITION xxv<br/><br/>1 Introduction 1<br/><br/>1.1 Five Important Practical Problems 2<br/><br/>1.2 Stochastic and Deterministic Dynamic Mathematical Models 6<br/><br/>1.3 Basic Ideas in Model Building 14<br/><br/>Appendix A1.1 Use of the R Software 17<br/><br/>Exercises 18<br/><br/>PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19<br/><br/>2 Autocorrelation Function and Spectrum of Stationary Processes 21<br/><br/>2.1 Autocorrelation Properties of Stationary Models 21<br/><br/>2.2 Spectral Properties of Stationary Models 34<br/><br/>Appendix A2.1 Link Between the Sample Spectrum and Autocovariance<br/><br/>Function Estimate 43<br/><br/>Exercises 44<br/><br/>3 Linear Stationary Models 47<br/><br/>3.1 General Linear Process 47<br/><br/>3.2 Autoregressive Processes 54<br/><br/>3.3 Moving Average Processes 68<br/><br/>3.4 Mixed Autoregressive--Moving Average Processes 75<br/><br/>Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82<br/><br/>Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84<br/><br/>Exercises 86<br/><br/>4 Linear Nonstationary Models 88<br/><br/>4.1 Autoregressive Integrated Moving Average Processes 88<br/><br/>4.2 Three Explicit Forms for the ARIMA Model 97<br/><br/>4.3 Integrated Moving Average Processes 106<br/><br/>Appendix A4.1 Linear Difference Equations 116<br/><br/>Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121<br/><br/>Appendix A4.3 ARIMA Processes with Added Noise 122<br/><br/>Exercises 126<br/><br/>5 Forecasting 129<br/><br/>5.1 Minimum Mean Square Error Forecasts and Their Properties 129<br/><br/>5.2 Calculating Forecasts and Probability Limits 135<br/><br/>5.3 Forecast Function and Forecast Weights 139<br/><br/>5.4 Examples of Forecast Functions and Their Updating 144<br/><br/>5.5 Use of State-Space Model Formulation for Exact Forecasting 155<br/><br/>5.6 Summary 162<br/><br/>Appendix A5.1 Correlation Between Forecast Errors 164<br/><br/>Appendix A5.2 Forecast Weights for any Lead Time 166<br/><br/>Appendix A5.3 Forecasting in Terms of the General Integrated Form 168<br/><br/>Exercises 174<br/><br/>PART TWO STOCHASTIC MODEL BUILDING 177<br/><br/>6 Model Identification 179<br/><br/>6.1 Objectives of Identification 179<br/><br/>6.2 Identification Techniques 180<br/><br/>6.3 Initial Estimates for the Parameters 194<br/><br/>6.4 Model Multiplicity 202<br/><br/>Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206<br/><br/>Exercises 207<br/><br/>7 Parameter Estimation 209<br/><br/>7.1 Study of the Likelihood and Sum-of-Squares Functions 209<br/><br/>7.2 Nonlinear Estimation 226<br/><br/>7.3 Some Estimation Results for Specific Models 236<br/><br/>7.4 Likelihood Function Based on the State-Space Model 242<br/><br/>7.5 Estimation Using Bayes’ Theorem 245<br/><br/>Appendix A7.1 Review of Normal Distribution Theory 251<br/><br/>Appendix A7.2 Review of Linear Least-Squares Theory 256<br/><br/>Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259<br/><br/>Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266<br/><br/>Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274<br/><br/>Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277<br/><br/>Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280<br/><br/>Exercises 280<br/><br/>8 Model Diagnostic Checking 284<br/><br/>8.1 Checking the Stochastic Model 284<br/><br/>8.2 Diagnostic Checks Applied to Residuals 287<br/><br/>8.3 Use of Residuals to Modify the Model 301<br/><br/>Exercises 303<br/><br/>9 Analysis of Seasonal Time Series 305<br/><br/>9.1 Parsimonious Models for Seasonal Time Series 305<br/><br/>9.2 Representation of the Airline Data by a Multiplicative (0 1 1) × (0 1 1)12 Model 310<br/><br/>9.3 Some Aspects of More General Seasonal ARIMA Models 325<br/><br/>9.4 Structural Component Models and Deterministic Seasonal Components 331<br/><br/>9.5 Regression Models with Time Series Error Terms 339<br/><br/>Appendix A9.1 Autocovariances for Some Seasonal Models 345<br/><br/>Exercises 349<br/><br/>10 Additional Topics and Extensions 352<br/><br/>10.1 Tests for Unit Roots in ARIMA Models 353<br/><br/>10.2 Conditional Heteroscedastic Models 361<br/><br/>10.3 Nonlinear Time Series Models 377<br/><br/>10.4 Long Memory Time Series Processes 385<br/><br/>Exercises 392<br/><br/>PART THREE TRANSFER FUNCTION AND MULTIVARIATE MODEL BUILDING 395<br/><br/>11 Transfer Function Models 397<br/><br/>11.1 Linear Transfer Function Models 397<br/><br/>11.2 Discrete Dynamic Models Represented by Difference Equations 404<br/><br/>11.3 Relation Between Discrete and Continuous Models 414<br/><br/>Appendix A11.1 Continuous Models with Pulsed Inputs 420<br/><br/>Appendix A11.2 Nonlinear Transfer Functions and Linearization 424<br/><br/>Exercises 426<br/><br/>12 Identification Fitting and Checking of Transfer Function Models 428<br/><br/>12.1 Cross-Correlation Function 429<br/><br/>12.2 Identification of Transfer Function Models 435<br/><br/>12.3 Fitting and Checking Transfer Function Models 446<br/><br/>12.4 Some Examples of Fitting and Checking Transfer Function Models 453<br/><br/>12.5 Forecasting with Transfer FunctionModels Using Leading Indicators 461<br/><br/>12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions 469<br/>Appendix A12.1 Use of Cross-Spectral Analysis for Transfer Function Model Identification 471<br/><br/>Appendix A12.2 Choice of Input to Provide Optimal Parameter Estimates 473<br/><br/>Exercises 477<br/><br/>13 Intervention Analysis Outlier Detection and Missing Values 481<br/><br/>13.1 Intervention Analysis Methods 481<br/><br/>13.2 Outlier Analysis for Time Series 488<br/><br/>13.3 Estimation for ARMA Models with Missing Values 495<br/><br/>Exercises 502<br/><br/>14 Multivariate Time Series Analysis 505<br/><br/>14.1 Stationary Multivariate Time Series 506<br/><br/>14.2 Vector Autoregressive Models 509<br/><br/>14.3 Vector Moving Average Models 524<br/><br/>14.4 Vector Autoregressive--Moving Average Models 527<br/><br/>14.5 Forecasting for Vector Autoregressive--Moving Average Processes 534<br/><br/>14.6 State-Space Form of the VARMA Model 536<br/><br/>14.7 Further Discussion of VARMA Model Specification 539<br/><br/>14.8 Nonstationarity and Cointegration 546<br/><br/>Appendix A14.1 Spectral Characteristics and Linear Filtering Relations for Stationary Multivariate Processes 552<br/><br/>Exercises 554<br/><br/>PART FOUR DESIGN OF DISCRETE CONTROL SCHEMES 559<br/><br/>15 Aspects of Process Control 561<br/><br/>15.1 Process Monitoring and Process Adjustment 562<br/><br/>15.2 Process Adjustment Using Feedback Control 566<br/><br/>15.3 Excessive Adjustment Sometimes Required by MMSE Control 580<br/><br/>15.4 Minimum Cost Control with Fixed Costs of Adjustment and Monitoring 582<br/><br/>15.5 Feedforward Control 588<br/><br/>15.6 Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes 599<br/><br/>Appendix A15.1 Feedback Control Schemes Where the Adjustment Variance Is Restricted 600<br/><br/>Appendix A15.2 Choice of the Sampling Interval 609<br/><br/>Exercises 613<br/><br/>PART FIVE CHARTS AND TABLES 617<br/><br/>COLLECTION OF TABLES AND CHARTS 619<br/><br/>COLLECTION OF TIME SERIES USED FOR EXAMPLES IN THE TEXT AND IN EXERCISES 625<br/><br/>REFERENCES 642<br/><br/>INDEX 659
650 #0 - Subject
Subject	Time-series analysis.
650 #0 - Subject
Subject	Prediction theory.
650 #0 - Subject
Subject	Transfer functions.
650 #0 - Subject
Subject	Feedback control systems
700 1# - Added Entry Personal Name
Added Entry Personal Name	Box, George E.P.
Relator Code	auth.
700 1# - Added Entry Personal Name
Added Entry Personal Name	Jenkins, Gwilym M.
Relator Code	auth.
700 1# - Added Entry Personal Name
Added Entry Personal Name	Reinsel, Gregory C.
Relator Code	auth.
700 1# - Added Entry Personal Name
Added Entry Personal Name	Ljung, Greta M.,
Relator Code	auth.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	REFERENCE STATISTICS

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		Dewey Decimal Classification		Not For Loan	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Reference Section	07/24/2023	K.M. Enterprise	S.X.U.K	R 519.55 TIM.Ed5	US9907	07/24/2023	9907	07/24/2023	REFERENCE STATISTICS
		Dewey Decimal Classification		Not For Loan	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Reference Section	09/25/2023	SEGMENT		R 519.55 TIM.Ed5.C1	US10370	09/25/2023	10370	09/25/2023	REFERENCE STATISTICS	Reference	13745.13