时间序列分析 预测与控制 英文版 第3版PDF电子书下载

1 INTRODUCTION 1

1.1 Four Important Practical Problems 2

1.1.1 Forecasting Time Series 2

1.1.2 Estimation of Transfer Functions 3

1.1.3 Analysis of Effects of Unusual Intervention Events To a System 4

1.1.4 Discrete Control Systems 5

1.2 Stochastic and Deterministic Dynamic Mathematical Models 7

1.2.1 Stationary and Nonstationary Stochastic Models for Forecasting and Control 7

1.2.2 Transfer Function Models 12

1.2.3 Models for Discrete Control Systems 14

1.3 Basic Ideas in Model Building 16

1.3.1 Parsimony 16

1.3.2 Iterative Stages in the Selection of a Model 16

Part 1 Stochastic Models and Their Forecasting 19

2 AUTOCORRELATION FUNCTION AND SPECTRUM OF STATIONARY PROCESSES 21

2.1 Autocorrelation Properties of Stationary Models 21

2.1.1 Time Series and Stochastic Processes 21

2.1.2 Stationary Stochastic Processes 23

2.1.3 Positive Definiteness and the Autocovariance Matrix 26

2.1.4 Autocovariance and Autocorrelation Functions 29

2.1.5 Estimation of Autocovariance and Autocorrelation Functions 30

2.1.6 Standard Error of Autocorrelation Estimates 32

2.2 Spectral Properties of Stationary Models 35

2.2.1 Periodogram of a Time Series 35

2.2.2 Analysis of Variance 36

2.2.3 Spectrum and Spectral Density Function 37

2.2.4 Simple Examples of Autocorrelation and Spectral Density Functions 41

2.2.5 Advantages and Disadvantages of the Autocorrelation and Spectral Density Functions 43

A2.1 Link Between the Sample Spectrum and Autocovariance Function Estimate 44

3 LINEAR STATIONARY MODELS 46

3.1 General Linear Process 46

3.1.1 Two Equivalent Forms for the Linear Process 46

3.1.2 Autocovariance Generating Function of a Linear Process 49

3.1.3 Stationarity and Invertibility Conditions for a Linear Process 50

3.1.4 Autoregressive and Moving Average Processes 52

3.2 Autoregressive Processes 54

3.2.1 Stationarity Conditions for Autoregressive Processes 54

3.2.2 Autocorrelation Function and Spectrum of Autoregressive Processes 55

3.2.3 First-Order Autoregressive(Markov)Process 58

3.2.4 Second-Order Autoregressive Process 60

3.2.5 Partial Autocorrelation Function 64

3.2.6 Estimation of the Partial Autocorrelation Function 67

3.2.7 Standard Errors ofPartial Autocorrelation Estimates 68

3.3 Moving Average Processes 69

3.3.1 Invertibility Conditions for Moving Average Processes 69

3.3.2 Autocorrelation Function and Spectrum of Moving Average Processes 70

3.3.3 First-Order Moving Average Process 72

3.3.4 Second-Order Moving Average Process 73

3.3.5 Duality Between Autoregressive and Moving Average Processes 75

3.4 Mixed Autoregressive-Moving Average Processes 77

3.4.1 Stationarity and Invertibility Properties 77

3.4.2 Autocorrelation Function and Spectrum of Mixed Processes 78

3.4.3 First-Order Autoregressive-First-Order Moving Average Process 80

3.4.4 Summary 83

A3.1 Autocovariances,Autocovariance Generating Function,and Stationarity Conditions for a General Linear Process 85

A3.2 RecursiveMethod for Calculating Estimates of Autoregressive Parameters 87

4 LINEAR NONSTATIONARY MODELS 89

4.1 Autoregressive Integrated Moving Average Processes 89

4.1.1 Nonstationary First-Order Autoregressive Process 89

4.1.2 General Model for a Nonstationary Process Exhibiting Homogeneity 92

4.1.3 General Form of the Autoregressive Integrated Moving A verage Process 96

4.2 Three Explicit Forms for the Autoregressive Integrated Moving Average Model 99

4.2.1 Difference Equation Form ofthe Model 99

4.2.2 Random Shock Form ofthe Model 100

4.2.3 Inverted Form of the Model 106

4.3 Integrated Moving Average Processes 109

4.3.1 Integrated Moving Average Process of Order (0,1,1) 110

4.3.2 Integrated Moving Average Process of Order(0,2,2) 114

4.3.3 General Integrated Moving Average Process of Order(0,d,q) 118

A4.1 Linear Difierence Equations 120

A4.2 IMA(0,1,1)Process With Deterministic Drift 125

A4.3 ARIMA Processes With Added Noise 126

A4.3.1 Sum of Two Independent Moving Average Processes 126

A4.3.2 Effect ofAdded Noise on the General Model 127

A4.3.3 Example for an IMA(0,1,1)Process with Added White Noise 128

A4.3.4 Relation Between the IMA(0,1,1) Process and a Random Walk 129

A4.3.5 Autocovariance Function of the General Model with Added Correlated Noise 129

5 FORECASTING 131

5.1 Minimum Mean Square Error Forecasts and Their Properties 131

5.1.1 Derivation of the Minimum Mean Square Error Forecasts 133

5.1.2 Three Basic Forms for the Forecast 135

5.2 Calculating and Updating Forecasts 139

5.2.1 Convenient Format for the Forecasts 139

5.2.2 Calculation of theψWeights 139

5.2.3 Use ofthe ψWeights in Updating the Forecasts 141

5.2.4 Calculation of the Probability Limits of the ForecastsatAny Lead Time 142

5.3 Forecast Function and Forecast Weights 145

5.3.1 Eventual Forecast Function Determined by the Autoregressive Operator 146

5.3.2 Role of the Moving Average Operator in Fixing the Initial Values 147

5.3.3 Lead l Forecast Weights 148

5.4 Examples of Forecast Functions and Their Updating 151

5.4.1 Forecasting an IMA(0,1,1) Process 151

5.4.2 Forecasting an IMA(0,2,2)Process 154

5.4.3 Forecasting a GeneralIMA(0,d,q)Process 156

5.4.4 Forecasting Autoregressive Processes 157

5.4.5 Forecasting a(1,0,1)Process 160

5.4.6 Forecasting a(1,1,1)Process 162

5.5 Use of State Space Model Formulation for Exact Forecasting 163

5.5.1 State Space Model Representation for the ARIMA Process 163

5.5.2 Kalman Filtering Relations for Use in Prediction 164

5.6 Summary 166

A5.1 Correlations Between Forecast Errors 169

A5.1.1 Autocorrelation Function of Forecast Errors at Different Origins 169

A5.1.2 Correlation Between Forecast Errors at the Same Origin with Different Lead Times 170

A5.2 Forecast Weights for Any Lead Time 172

A5.3 Forecasting in Terms of the General Integrated Form 174

A5.3.1 General Method of Obtaining the Integrated Form 174

A5.3.2 Updating the General Integrated Form 176

A5.3.3 Comparison with the Discounted Least Squares Method 176

Part Ⅱ Stochastic Model Building 181

6 MODELIDENTIFICATION 183

6.1 Obiectives of Identification 183

6.1.1 Stages in the Identification Procedure 184

6.2 Identification Techniques 184

6.2.1 Use of the Autocorrelation and Partial Autocorrelation Functions in Identification 184

6.2.2 Standard Errors for Estimated Autocorrelations and Partial Autocorrelations 188

6.2.3 Identification of Some Actual Time Series 188

6.2.4 Some Additional Model Identification Tools 197

6.3 Initial Estimates for the Parameters 202

6.3.1 Uniqueness of Estimates Obtained from the Autocovariance Function 202

6.3.2 Initial Estimates for Moving Average Processes 202

6.3.3 Initial Estimates for Autoregressive Processes 204

6.3.4 Initial Estimates for Mixed Autoregressive-Moving Average Processes 206

6.3.5 Choice Between Stationary and Nonstationary Models in Doubtful Cases 207

6.3.6 More Formal Tests for Unit Roots in ARIMA Models 208

6.3.7 Initial Estimate of Residual Variance 211

6.3.8 Approximate Standard Error for ？ 212

6.4 Model Multiplicity 214

6.4.1 Multiplicity of Autoregressive-Moving Average Models 214

6.4.2 Multiple Moment Solutions for Moving Average Parameters 216

6.4.3 Use of the Backward Process to Determine Starting Values 218

A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 218

A6.2 General Method for Obtaining Initial Estimates of the Parameters of a Mixed Autoregressive-Moving Average Process 220

7 MODEL ESTIMATION 224

7.1 Study of the Likelihood and Sum of Squares Functions 224

7.1.1 Likelihood Function 224

7.1.2 Conditional Likelihood for an ARIMA Process 226

7.1.3 Choice of Starting Values for Conditional Calculation 227

7.1.4 Unconditional Likelihood;Sum of Squares Function;Least Squares Estimates 228

7.1.5 General Procedure for Calculating the Unconditional Sum of Squares 233

7.1.6 GraphicalStudy of the Sum of Squares Function 238

7.1.7 Description of"Well-Behaved"Estimation Situations,Confidence Regions 241

7.2 Nonlinear Estimation 248

7.2.1 General Method of Approach 248

7.2.2 Numerical Estimates of the Derivatives 249

7.2.3 Direct Evaluation of the Derivatives 251

7.2.4 General Least Squares Algorithm for the Conditional Model 252

7.2.5 Summary of Models Fitted to Series A to F 255

7.2.6 Large-Sample Information Matrices and Covariance Estimates 256

7.3 Some Estimation Results for Specific Models 259

7.3.1 Autoregressite Processes 260

7.3.2 Moving Average Processes 262

7.3.3 Mixed Processes 262

7.3.4 Separation of Linear and Nonlinear Components in Estimation 263

7.3.5 Parameter Redundancy 264

7.4 Estimation Using Bayes'Theorem 267

7.4.1 Bayes'Theorem 267

7.4.2 Bayesian Estimation ofParameters 269

7.4.3 Autoregressive Processes 270

7.4.4 Moving Average Processes 272

7.4.5 Mixed processes 274

7.5 Likelihood Function Based on The State Space Model 275

A7.1 Review of Normal Distribution Theory 279

A7.1.1 Partitioning of a Positive-Definite Quadratic Form 279

A7.1.2 Two Useful Integrals 280

A7.1.3 Normal Distribution 281

A7.1.4 Student's t-Distribution 283

A7.2 Review of Linear Least Squares Theory 286

A7.2.1 Normal Equations 286

A7.2.2 Estimation ofResidual Variance 287

A7.2.3 Covariance Matrix ofEstimates 288

A7.2.4 Confidence Regions 288

A7.2.5 Correlated Errors 288

A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 289

A7.4 Exact Likelihood Function for an Autoregressive Process 296

A7.5 Examples of the Effect of Parameter Estimation Errors on Probability Limits for Forecasts 304

A7.6 Special Note on Estimation of Moving Average Parameters 307

8 MODEL DIAGNOSTIC CHECKING 308

8.1 Checking the Stochastic Model 308

8.1.1 General Philosophy 308

8.1.2 Overfitting 309

8.2 Diagnostic Checks Applied to Residuals 312

8.2.1 Autocorrelation Check 312

8.2.2 Portmanteau Lack-of-Fit Test 314

8.2.3 Model Inadequacy Arising from Changes in Parameter values 317

8.2.4 Score Tests for Model Checking 318

8.2.5 Cumulative Periodogram Check 321

8.3 Use of Residuals to Modify the Model 324

8.3.1 Nature of the Correlations in the Residuals When an Incorrect Model Is Used 324

8.3.2 Use of Residuals to Modify the Model 325

9 SEASONAL MODELS 327

9.1 Parsimonious Models for Seasonal Time Series 327

9.1.1 Fitting versus Forecasting 328

9.1.2 Seasonal Models Involving Adaptive Sines and Cosines 329

9.1.3 General Multiplicative Seasonal Model 330

9.2 Representation of the Airline Data by a Multiplicative (0,1,1)×(0,1,1)12 Seasonal Model 333

9.2.1 Multiplicative(0,1,1)x (0,1,1)12 Model 333

9.2.2 Forecasting 334

9.2.3 Identification 341

9.2.4 Estimation 344

9.2.5 Diagnostic Checking 349

9.3 Some Aspects of More General Seasonal Models 351

9.3.1 Multiplicative and Nonmultiplicative Models 351

9.3.2 Identification 353

9.3.3 Estimation 355

9.3.4 Eventual Forecast Functions for Various Seasonal Models 355

9.3.5 Choice of Transformation 358

9.4 Structural Component Models and Deterministic Seasonal Components 359

9.4.1 Deterministic Seasonal and Trend Components and Common Factors 360

9.4.2 Models with Regression Terms and Time Series Error Terms 361

A9.1 Autocovariances for Some Seasonal Models 366

Part Ⅲ Transfer Function Model Building 371

10 TRANSFER FUNCTION MODELS 373

10.1 Linear Transfer Function Models 373

10.1.1 Discrete Transfer Function 374

10.1.2 Continuous Dynamic Models Represented by Differential Equations 376

10.2 Discrete Dynamic Models Represented by Difference Equations 381

10.2.1 General Form of the Difference Equation 381

10.2.2 Nature of the Transfer Function 383

10.2.3 First-and Second-Order Discrete Transfer Function Models 384

10.2.4 Recursive Computation of Output for Any Input 390

10.2.5 Transfer Function Models with Added Noise 392

10.3 Relation Between Discrete and Continuous Models 392

10.3.1 Response to a Pulsed Input 393

10.3.2 Relationships for First-and Second-Order Coincident Systems 395

10.3.3 Approximating General Continuous Models by Discrete Models 398

A10.1 Continuous Models With Pulsed Inputs 399

A10.2 Nonlinear Transfer Functions and Linearization 404

11 IDENTIFICATION,FITTING,AND CHECKING OF TRANSFER FUNCTION MODELS 407

11.1 Cross Correlation Function 408

11.1.1 Properties of the Cross Covariance and Cross Correlation Functions 408

11.1.2 Estimation of the Cross Covariance and Cross Correlation Functions 411

11.1.3 Approximate Standard Errors of Cross Correlation Estimates 413

11.2 Identification of Transfer Function Models 415

11.2.1 Identification of Transfer Function Models by Prewhitening the Input 417

11.2.2 Example ofthe Identification of a Transfer Function Model 419

11.2.3 Identification of the Noise Model 422

11.2.4 Some General Considerations in Identifying Transfer Function Models 424

11.3 Fitting and Checking Transfer Function Models 426

11.3.1 Conditional Sum of Squares Function 426

11.3.2 Nonlinear Estimation 429

11.3.3 Use of Residuals for Diagnostic Checking 431

11.3.4 Specific Checks Applied to the Residuals 432

11.4 Some Examples of Fitting and Checking Transfer Function Models 435

11.4.1 Fitting and Checking of the Gas Furnace Model 435

11.4.2 Simulated Example with Two Inputs 441

11.5 Forecasting Using Leading Indicators 444

11.5.1 Minimum Mean Square Error Forecast 444

11.5.2 Forecast ofCO2 Outputfrom Gas Furnace 448

11.5.3 Forecast of Nonstationary Sales Data Using a Leading Indicator 451

11.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions 453

A11.1 Use of Cross Spectral Analysis for Transfer Function Model Identification 455

A11.1.1 Identification ofSingle Input Transfer Function Models 455

A11.1.2 Identification of Multiple Input Transfer Function Models 456

A 11.2 Choice of Input to Provide Optimal Parameter Estimates 457

A11.2.1 Design of Optimal Inputs for a Simple System 457

A11.2.2 Numerical Example 460

12 INTERVENTION ANALYSIS MODELS AND OUTLIER DETECTION 462

12.1 Intervention Analysis Methods 462

12.1.1 Modelsfor Intervention Analysis 462

12.1.2 Example of Intervention Analysis 465

12.1.3 Nature of the MLE for a Simple Level Change Parameter Model 466

12.2 Outlier Analysis for Time Series 469

12.2.1 Models for Additive and Innovational Outliers 469

12.2.2 Esti？ation of Outlier Effect for Known Timing of the Outlier 470

12.2.3 Iterative Procedure for Outlier Detection 471

12.2.4 Examples of Analysis of Outliers 473

12.3 Estimation for ARMA Models With Missing Values 474

Part Ⅳ Design of Discrete Control Schemes 481

13 ASPECTS OF PROCESS CONTROL 483

13.1 Process Monitoring and Process Adjustment 484

13.1.1 Process Monitoring 484

13.1.2 Process Adjustment 487

13.2 Process Adjustment Using Feedback Control 488

13.2.1 Feedback Adjustment Chart 489

13.2.2 Modeling the Feedback Loop 492

13.2.3 Simple Models for Disturbances and Dynamics 493

13.2.4 General Minimum Mean Square Error Feedback Control Schemes 497

13.2.5 Manual Adjustment for Discrete Proportional-Integral Schemes 499

13.2.6 Complementary Roles of Monitoring and Adjustment 503

13.3 Excessive Adjustment Sometimes Required by MMSE Control 505

13.3.1 Constrained Control 506

13.4 Minimum Cost Control With Fixed Costs of Adjustment And Monitoring 508

13.4.1 Bounded Adjustment Scheme for Fixed Adjustment Cost 508

13.4.2 Indirect Approach for Obtaining a Bounded Adjustment Scheme 510

13.4.3 Inclusion of the Cost of Monitoring 511

13.5 Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes 514

A13.1 Feedback Control Schemes Where the Adiustment Variance Is Restricted 516

A13.1.1 Derivation of Optimal Adjustment 517

A13.2 Choice of the Sampling Interval 526

A13.2.1 Illustration of the Effect of Reducing Sampling Frequency 526

A13.2.2 Sampling an lMA(0,1,1)Process 526

Part Ⅴ Charts and Tables 531

COLLECTION OF TABLES AND CHARTS 533

COLLECTION OF TIME SERIES USED FOR EXAMPLES IN THE TEXT AND IN EXERCISES 540

REFERENCES 556

Part Ⅵ EXERCISES AND PROBLEMS 569

INDEX 589