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Introduction to Time Series Analysis and Forecasting, Second Edition
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Table of Contents

preface xi

1 Introduction to Forecasting 1

1.1 The Nature and Uses of Forecasts 1

1.2 Some Examples of Time Series 6

1.3 The Forecasting Process 13

1.4 Data for Forecasting 16

1.4.1 The Data Warehouse 16

1.4.2 Data Cleaning 18

1.4.3 Imputation 18

1.5 Resources for Forecasting 19

Exercises 20

2 Statistics Background for Forecasting 25

2.1 Introduction 25

2.2 Graphical Displays 26

2.2.1 Time Series Plots 26

2.2.2 Plotting Smoothed Data 30

2.3 Numerical Description of Time Series Data 33

2.3.1 Stationary Time Series 33

2.3.2 Autocovariance and Autocorrelation Functions 36

2.3.3 The Variogram 42

2.4 Use of Data Transformations and Adjustments 46

2.4.1 Transformations 46

2.4.2 Trend and Seasonal Adjustments 48

2.5 General Approach to Time Series Modeling and Forecasting 61

2.6 Evaluating and Monitoring Forecasting Model Performance 64

2.6.1 Forecasting Model Evaluation 64

2.6.2 Choosing Between Competing Models 74

2.6.3 Monitoring a Forecasting Model 77

2.7 R Commands for Chapter 2 84

Exercises 96

3 Regression Analysis and Forecasting 107

3.1 Introduction 107

3.2 Least Squares Estimation in Linear Regression Models 110

3.3 Statistical Inference in Linear Regression 119

3.3.1 Test for Significance of Regression 120

3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients 123

3.3.3 Confidence Intervals on Individual Regression Coefficients 130

3.3.4 Confidence Intervals on the Mean Response 131

3.4 Prediction of New Observations 134

3.5 Model Adequacy Checking 136

3.5.1 Residual Plots 136

3.5.2 Scaled Residuals and PRESS 139

3.5.3 Measures of Leverage and Influence 144

3.6 Variable Selection Methods in Regression 146

3.7 Generalized and Weighted Least Squares 152

3.7.1 Generalized Least Squares 153

3.7.2 Weighted Least Squares 156

3.7.3 Discounted Least Squares 161

3.8 Regression Models for General Time Series Data 177

3.8.1 Detecting Autocorrelation: The Durbin-Watson Test 178

3.8.2 Estimating the Parameters in Time Series Regression Models 184

3.9 Econometric Models 205

3.10 R Commands for Chapter 3 209

Exercises 219

4 Exponential Smoothing Methods 233

4.1 Introduction 233

4.2 First-Order Exponential Smoothing 239

4.2.1 The Initial Value, y0 241

4.2.2 The Value of 𝜆 241

4.3 Modeling Time Series Data 245

4.4 Second-Order Exponential Smoothing 247

4.5 Higher-Order Exponential Smoothing 257

4.6 Forecasting 259

4.6.1 Constant Process 259

4.6.2 Linear Trend Process 264

4.6.3 Estimation of 𝜎2e 273

4.6.4 Adaptive Updating of the Discount Factor 274

4.6.5 Model Assessment 276

4.7 Exponential Smoothing for Seasonal Data 277

4.7.1 Additive Seasonal Model 277

4.7.2 Multiplicative Seasonal Model 280

4.8 Exponential Smoothing of Biosurveillance Data 286

4.9 Exponential Smoothers and Arima Models 299

4.10 R Commands for Chapter 4 300

Exercises 311

5 Autoregressive Integrated Moving Average (Arima) Models 327

5.1 Introduction 327

5.2 Linear Models for Stationary Time Series 328

5.2.1 Stationarity 329

5.2.2 Stationary Time Series 329

5.3 Finite Order Moving Average Processes 333

5.3.1 The First-Order Moving Average Process, MA(1) 334

5.3.2 The Second-Order Moving Average Process, MA(2) 336

5.4 Finite Order Autoregressive Processes 337

5.4.1 First-Order Autoregressive Process, AR(1) 338

5.4.2 Second-Order Autoregressive Process, AR(2) 341

5.4.3 General Autoregressive Process, AR(p) 346

5.4.4 Partial Autocorrelation Function, PACF 348

5.5 Mixed Autoregressive-Moving Average Processes 354

5.5.1 Stationarity of ARMA(p, q) Process 355

5.5.2 Invertibility of ARMA(p, q) Process 355

5.5.3 ACF and PACF of ARMA(p, q) Process 356

5.6 Nonstationary Processes 363

5.6.1 Some Examples of ARIMA(p, d, q) Processes 363

5.7 Time Series Model Building 367

5.7.1 Model Identification 367

5.7.2 Parameter Estimation 368

5.7.3 Diagnostic Checking 368

5.7.4 Examples of Building ARIMA Models 369

5.8 Forecasting Arima Processes 378

5.9 Seasonal Processes 383

5.10 Arima Modeling of Biosurveillance Data 393

5.11 Final Comments 399

5.12 R Commands for Chapter 5 401

Exercises 412

6 Transfer Functions and Intervention Models 427

6.1 Introduction 427

6.2 Transfer Function Models 428

6.3 Transfer Function-Noise Models 436

6.4 Cross-Correlation Function 436

6.5 Model Specification 438

6.6 Forecasting with Transfer Function-Noise Models 456

6.7 Intervention Analysis 462

6.8 R Commands for Chapter 6 473

Exercises 486

7 Survey of Other Forecasting Methods 493

7.1 Multivariate Time Series Models and Forecasting 493

7.1.1 Multivariate Stationary Process 494

7.1.2 Vector ARIMA Models 494

7.1.3 Vector AR (VAR) Models 496

7.2 State Space Models 502

7.3 Arch and Garch Models 507

7.4 Direct Forecasting of Percentiles 512

7.5 Combining Forecasts to Improve Prediction Performance 518

7.6 Aggregation and Disaggregation of Forecasts 522

7.7 Neural Networks and Forecasting 526

7.8 Spectral Analysis 529

7.9 Bayesian Methods in Forecasting 535

7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures 542

7.11 R Commands for Chapter 7 545

Exercises 550

Appendix A Statistical Tables 561

Appendix B Data Sets for Exercises 581

Appendix C Introduction to R 627

Bibliography 631

Index 639

About the Author

Douglas C. Montgomery, PhD, is Regents' Professor and ADUFoundation Professor of Engineering at Arizona StateUniversity. With over 35 years of academic and consultingexperience, Dr. Montgomery has authored or coauthored over 250journal articles and 13 books. His research interests includedesign and analysis of experiments, statistical methods for processmonitoring and optimization, and the analysis of time-orienteddata. Cheryl L. Jennings, PhD, is a Process Design Consultant withBank of America. An active member of both the American StatisticalAssociation and the American Society for Quality, her areas ofresearch and profession interest include Six Sigma, modeling andanalysis, and process control and improvement. Murat Kulahci, PhD, is Associate Professor of Informaticsand Mathematical Modelling at the Technical University of Denmark.The author of over 30 journal articles, Dr. Kulahci sresearch interests include time series analysis, design ofexperiments, and statistical process control and monitoring.

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