Principles of business forecasting:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Wessex Press, Inc.
[2017]
|
| Ausgabe: | 2nd edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 544 Seiten, 23 verschieden gezählte Seiten Illustrationen, Diagramme |
| ISBN: | 9780999064917 9780999064900 |
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| adam_text | BRIEF CONTENTS Topics marked with an * are advanced and may be omittedfor more introductory courses. Preface xiii CHAPTER 1 Forecasting, the Why and the How 1 CHAPTER 2 Basic Tools for Forecasting 23 CHAPTER 3 Forecasting Non-Seasonal Series CHAPTER 4 Seasonal Series: Forecasting and Decomposition 99 CHAPTER 5 State-Space Models for Time Series CHAPTER 6 Autoregressive Integrated Moving Average (ARIMA) Models CHAPTER 7 Simple Linear Regression for Forecasting 207 CHAPTER 8 Multiple Regression for Time Series CHAPTER 9 Model Building 277 CHAPTER 10 Advanced Methods of Forecasting* CHAPTER 11 Judgment-Based Forecasting 383 CHAPTER 12 Putting Forecasting Methods to Work 421 CHAPTER 13 Forecasting in Practice 501 61 131 247 325 Glossary GLI Index INI The Appendices are located on the textbook companion website. appendix A Basic Statistical Concepts appendix в Forecasting Software appendix c Forecasting in R: Tutorial and Examples 155
CONTENTS Topics marked with an * are advanced and may be omittedfor more introductory courses. Preface xiii chapter 1 Forecasting, the Why and the How Introduction 1.1 Why Forecast? 3 1.1.1 Purpose 3 1.1.2 Information 4 1.1.3 Value 4 1.1.4 Analysis 5 1.1.5 System 5 1.1.6 Evaluation 5 1.2 What and Why Do Organizations Forecast? 1.3 Examples of Forecasting Problems 8 1.3.1 Retail Sales 8 1.3.2 Seasonal Patterns for Retail Sales 9 1.3.3 UK Road Accidents 11 1.3.4 Airline Travel 12 1.3.5 Sports Forecasting: Soccer (a.k.a. Football!) 13 1.3.6 Sports Forecasting: A Cross-Sectional Example — Baseball Salaries 1.3.7 Random (Stochastic) Processes 15 1.4 How to Forecast 1.5 Forecasting Step by Step 1.6 Computer Packages for Forecasting 1.7 Data Sources 1.8 The Rest of the Book Summary 14 16 17 18 19 19 Mlnicase 1.2 Long-Term Growth Minicase 1.3 Sales Forecasting 19 20 20 Mlnicase 1.4 Adjusting for Inflation References 20 20 Appendix 1A Model-Based Probability 2 7 15 Minlcase 1.1 Inventory Planning chapter 1 2 Basic Tools for Forecasting Introduction 21 23 24 2.1 Types of Data 24 2.1.1 Use of Large Databases 2.2 Time Series Plots 26 2.2.1 Seasonal Plots 2.3 Scatterplots 2.4 Summarizing the Data 31 2.4.1 Notational Conventions 31 2.4.2 Measures of Average 32 2.4.3 Measures of Variation 33 2.4.4 Assessing Variability 35 2.4.5 An Example: Forecasts for the German Economy 2.5 Correlation 25 28 29 36 37 v
vi Principles of Business Forecasting 2e 2.6 2.7 Transformations 39 2.6.1 Differences and Growth Rates 2.6.2 The Log Transform 42 How to Evaluate Forecasting Accuracy 43 2.7.1 Measures of Forecasting Accuracy for Time Series 2.7.2 Measures of Absolute Error 47 2.8 Prediction Intervals 50 2.8.1 Using the Normal Distribution 51 2.8.2 Empirical Prediction Intervals 52 2.8.3 Prediction Intervals: Summary 52 2.9 Basic Principles of Data Analysis Summary 55 Exercises 55 Minicase 2.1 Baseball Salaries 58 Minicase 2.2 Whither Walmart? 58 Minicase 2.3 Economic Recessions References CHAPTER 3 40 45 53 59 59 Forecasting Non-Seasonal Series 61 Introduction 62 Software 62 3.1 Method or Model? 63 3.1.1 A Forecasting Model 3.2 Extrapoiative Methods 64 3.2.1 Extrapolation of the Mean Value 3.2.2 Use of Moving Averages 67 3.3 Simple 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 63 65 Exponentiai Smoothing 68 Forecasting with the EWMA, or Simple Exponential Smoothing The Exponential Smoothing Macro (ESM) 72 The Use of Hold-Out Samples 74 The Use of a Rolling Origin 75 Some General Comments 77 3.4 Linear Exponential Smoothing 77 3.4.1 Basic Structure for LES 78 3.4.2 Updating Relationships 79 3.4.3 Starting Values 79 3.5 Exponential Smoothing with a Damped Trend 3.5.1 Choice of Method 83 3.6 Other Approaches to Trend Forecasting 84 3.6.1 Brown’s Method of Double Exponential Smoothing (DES) 3.6.2 SES with (Constant) Drift: The Theta Method 84 3.6.3 Tracking Signals 85 3.6.4 Linear Moving Averages 85 3.7 The Use of Transformations 85 3.7.1 The Log Transform 86 3.7.2 The Use of Growth Rates 87 3.7.3 The Box-Cox
Transformations 88 3.8 Prediction Intervals 3.9 Method Selection 3.10 Principles of Extrapolative Methods Summary 94 Exercises 94 90 92 Minicase 3.1 Job Openings 93 96 Minicase 3.2 The Evolution of Walmart 96 Minicase 3.3 Volatility in the Dow Jones Index References 97 Appendix ЗА Excel Macra 98 97 82 84 70
Contents CHAPTER 4 Seasonal Series: Forecasting and Decomposition Introduction 4.1 Components of a Time Series 4.2 Forecasting Purely Seasonal Series 103 4.2.1 Purely Seasonal Exponential Smoothing 105 4.3 Forecasting Using a Seasonal Decomposition 106 4.4 Pure Decomposition 109 4.5* The Census X-13 Decomposition The Holt-Winters Seasonal Smoothing Methods 4.6.1 The Additive Holt-Winters Method 112 4.6.2* Starting Values 114 4.7 The Multiplicative Holt-Winters Method 118 4.7.1* Starting Values 119 4.7.2 Purely Multiplicative Schemes 119 4.8* Calculations Using R 4.9 Weekly Data 121 4.9.1 Multiple Seasonalities 4.10 Prediction Intervals 4.11 Principles for Seasonal Methods Summary 125 Exercises 126 111 124 124 125 128 Minicase 4.2 Automobile Production Minicase 4.3 U.S. Retail Sales Minicase 4.4 UK Retail Sales Minicase 4.5 Newspaper Sales 128 128 128 129 129 State-Space Models for Time Series Introduction 5.1 112 120 Minicase 4.1 Walmart Sales CHAPTER 5 101 4.6 References 99 100 131 132 State-Space Models for Exponential Smoothing 133 5.1.1 The SES Forecasting Framework 133 5.1.2 A State-Space Model for Simple Exponential Smoothing 5.1.3 A Class of State-Space Models 135 133 5.2 The Random Error Term 5.3 Prediction Intervals from State-Space Models 137 5.3.1 Prediction Intervals for the Additive Holt-Winters Model (A, A, A) 136 5.4 Model Selection 141 5.4.1 Use of a Hold-Out Sample 5.4.2 Information Criteria 143 5.4.3 Automatic Model Selection 5.5 Outliers 5.6 State-Space Modeling Principles Summary 149 Exercises 149 141 145 145 Minicase 5.1 Analysis of UK Retail Sales 148
151 Minicase 5.2 Prediction Intervals for WFJ Sales References 151 152 Appendix 5A* Derivation of Forecast Means and Variances Appendix 5B The Holt-Winters Multiplicative Scheme 153 152 140 vii
viii Principles of Business Forecasting 2e CHAPTER 6 Autoregressive Integrated Moving Average (ARIMA) Models Introduction 6.1 156 The Sample Autocorrelation Function 6.1.1 6.2 Model Assumptions Autoregressive Moving Average (ARMA) Models 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5* 6.2.6* 6.3 Model Estimation and Differencing Model Diagnostics 6.9* Seasonal ARIMA Models State-Space and ARIMA Models 192 192 From ARIMA to a State-Space Form 6.10.1 6.11 186 189 Forecasts for Seasonal Models 6.9.1 184 184 Prediction Intervals 185 Forecasting Using Transformations 6.10* GARCH Models The GARCH (1,1) Model 196 199 ARIMA Models 199 GARCH Models 200 Summary 200 Exercises 201 Minicase 6.1 Analysis of UK Retail Sales Minicase 6.2 Run Mileage References 194 195 Principles of ARIMA Modeling 6.11.1 6.11.2 202 203 203 Appendix 6A* Mean and Variance for AR(1) Scheme CHAPTER 7 Simple Linear Regression for Forecasting Introduction 7.1 The Method of Ordinary Least Squares (OLS) 216 Preliminary Data Analysis 217 The Regression Model 219 How Good Is the Fitted Line? 7.4.1 7.4.2 209 What Is Regression Analysis? 210 A Case Study on the Price of Gasoline 7.3.1 7.3.2 7.4 207 208 Fitting a Regression Line by Ordinary Least Squares (OLS) 7.2.1 7.3 204 Relationships between Variables: Correlation and Causation 7.1.1 7.2 181 182 Forecasting with ARIMA Models 6.8.1 176 178 Dealing with the “Great Recession 6.7.1 6.7.2 6.8 170 The կսոց-Box-Pieroe Test 180 Diagnostics, Overfitting, and Model Simplification The Lag Operator 182 Outliers Again 6.6.1 6.7 167 Should We Assume Stationarity? 173 Examples of Nonstationary
Series 173 Model Choice Using Information Criteria How Much Differencing? 177 Formal Tests for Differencing 177 6.5.1 6.5.2 6.5.3* 6.6 163 The Random Walk 167 Nonstationary Series 168 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.5 159 The Rrst-Order Autoregressive Model 160 Higher Order Autoregressive Models 161 Pure Moving Average (MA) Models 162 Mixed Autoregressive Moving Average (ARMA) Models Partial Autocorrelations 163 Model Choice Using the ACF and PACF 166 An Introduction to Nonstationary Series 6.3.1 6.3.2 6.4 156 158 220 The Standard Error of Estimate 221 The Coefficient of Determination 222 213 211 155
Contents 7.5 The Statistical Framework for Regression 7.5.1 The Linear Model 223 7.5.2 Parameter Estimates 225 7.6 Testing the Slope 226 7.6.1 P-Values 228 7.6.2 Interpreting the Slope Coefficient 7.6.3 Transformations 231 223 230 7.7 Forecasting Using Simple Linear Regression 232 7.7.1 The Point Forecast 232 7.7.2 Prediction Intervals 233 7.7.3 An Approximate Prediction Interval 235 7.7.4 Forecasting More than One Period Ahead 236 7.8 Forecasting Using Leading Indicators Summary 238 Exercises 238 Minicase 7.1 Gasoline Prices Revisited 237 240 Minicase 7.2 Consumer Confidence and Unemployment Minicase 7.3 Baseball Salaries Revisited References 240 240 241 Appendix 7A Derivation of Ordinary Least Squares Estimators Appendix 7B Computing Р-Values in Excel and R Appendix 7C Computing Prediction Intervals CHAPTER 8 244 Multiple Regression for Time Series Introduction 241 243 247 248 8.1 Graphical Analysis and Preliminary Model Development 8.2 The Multiple Regression Model 250 8.2.1 The Method of Ordinary Least Squares (OLS) 8.3 Testing the Overall Model 252 8.3.1 The F-Test for Multiple Variables 252 8.3.2 ANOVA in Simple Regression 254 8.3.3 S and Adjusted R2 254 8.4 Testing Individual Coefficients 256 8.4.1 Case Study: Baseball Salaries 257 8.4.2 Testing a Group of Coefficients 259 8.5 Checking the Assumptions 260 8.5.1 Analysis of Residuals for Gas Price Data 8.6 Forecasting with Multiple Regression 265 8.6.1 The Point Forecast 265 8.6.2 Prediction Intervals 266 8.6.3 Forecasting More than One Period Ahead 8.7 Principles of Regression Summary 269 Exercises 269 249 251 263
268 268 Minicase 8.1 The Volatility of Google Stock 271 Minicase 8.2 Forecasting Natural Gas Consumption for the DC Metropolitan Area Minicase 8.3 U.S. Retail Food Service Sales Minicase 8.4 U.S. Automobile Sales References 273 274 Appendix 8A The Durbin-Watson Statistic 274 272 272 ix
x Principles of Business Forecasting 2e CHAPTER 9 Model Building Introduction 277 278 9.1 Indicator (Dummy) Variables 279 9.1.1 Seasonal Indicators 282 9.2 Autoregressive Models 9.3 Models with Both Autoregressive and Regression Components 9.4 Selection of Variables 288 9.4.1 Forward, Backward, and Stepwise Selection: Models of the Price of Gasoline 9.4.2 Searching All Possible Models: Best Subset Regression 292 9.4.3 Using a Hold-Out Sample to Compare Models 294 9.4.4 A Regression Model with Autoregressive Errors 295 285 9.5 Multicollinearity and Variable Selection 9.5.1 Use of Differences 298 9.5.2 The Lasso Method 300 9.6 Nonlinear Models 301 9.6.1 Polynomial Schemes 302 9.6.2 Nonlinear Transformations 303 9.6.3 Intrinsically Nonlinear Models 305 9.6.4 Changing Variances and the Use of Logarithmic Models 9.7 Outliers and Leverage 307 9.7.1 Leverage Points and What to Do About Them 308 9.7.2 The Effects of Outliers 309 9.7.3 The Role of Outliers and Leverage Points: A Summary 9.8 Intervention Analysis 9.9 Structural Change and Model Simplification 9.9.1 Model Simplification 316 9.10 An Update on Forecasting 9.11 Principles of Regression Model Building Summary 318 Exercises 319 Class Assessment 295 305 311 311 312 316 317 320 Mlnicase 9.1 An Econometric Analysis of Unleaded Gasoline Prices Minicase 9.2 The Effectiveness of Seat-Belt Legislation References chapter ю 322 323 324 Advanced Methods of Forecasting* Introduction 10.1 286 325 326 Predictive Classification 326 10.1.1 Evaluating the Accuracy of the Predictive Classifications 10.1.2 A Comment 333 10.2 Classification
and Regression Trees 333 10.2.1 Performance Measures: An Example 334 10.2.2 Computer Ownership Example Revisited 335 10.3 Logistic Regression 338 10.3.1 Issues In Logistic Regression Modeling 10.4 Neural 10.4.1 10.4.2 10.4.3 329 340 Network Methods 342 A Cross-Sectional Neural Network Analysis 350 A Time Series Neural Network Analysis: Modeling UK Retail Sales Neural Networks: A Summary 357 10.5 Vector Autoregressive (VAR) Models 357 10.5.1 Forecasting with a VAR Model 361 10.6 Principles: Predictive Classification, Neural Nets, and VAR Modeling Summary 363 Exercises 364 352 362 289
Contents Minicase 10.1 KMI BioPharma, Inc.: Biocide 366 The Market The New Product The Market Segmentation Study References 370 Appendix 10A PcGive Analysis of Unleaded Gasoline Price Data Appendix 10B The Effects of Nonstationary Data Appendix 10C Differencing and Unit Roots 376 Appendix 10D Introduction to Cointegration 378 Appendix 10E Modeling with Nonstationary Data: A Summary chapter 11 Judgment-Based Forecasting Introduction 383 11.1 Judgmental or Quantitative Forecasting? 11.1.1 An Appraisal 390 11.2 Judgmental Methods 390 11.2.1 The Single Expert (or Unaided Judgment) 390 11.2.2 Expert and Group Opinion 391 11.2.3 Jury of Expert Opinion 392 11.2.4 Sales Force Projections (or Sales Force Composite) 11.2.5 Customer Surveys 394 11.2.6 Use of Analogies 394 385 11.3 The Delphi Method 11.4 Forecasting Using Prediction Markets 401 11.4.1 The Structure of a Prediction Market 401 11.4.2 How Might Prediction Markets Be Used in Business? 11.4.3 Usefulness of Prediction Markets 402 393 397 402 11.5 Assessing Uncertainty Judgmentally 403 11.5.1 Assessing Percentage Points and Probabilities 403 11.5.2 Decomposition 405 11.5.3 Combining Judgmental Forecasts 406 11.5.4 Assessing the Accuracy of Qualitative Predictions 407 11.6 The Use of Scenarios 408 11.6.1 Role-Playing 410 11.7 Judgmental Forecasting Principles Summary 413 Exercises 414 References 411 416 416 Appendix 11A Delphi Software 418 Appendix 11В Debiasing Forecasts 12 381 384 Minicase 11.1 chapter 371 375 419 Putting Forecasting Methods to Work 421 Introduction 422 12.1 Evaluating a Forecasting Process 422 12.1.1
Evaluating Forecasting Methods: Forecasting Competitions 423 12.1.2 Combining Forecasting Methods or Choosing among Methods 426 12.2 The Role of Forecasting Support Systems 12.2.1 Forecast Adjustment 432 12.3 Operations 435 12.3.1 Data Issues in Supply Chain Forecasting 436 12.3.2 Supply Chain Forecasting and the Bullwhip Effect 12.3.3 Hierarchical Forecasting 439 12.3.4* Forecasts and Time Aggregation 442 12.3.5 Demand Data and Intermittent Demand 445 12.3.6 Operations Forecasting: Summary 448 429 437 x¡
xii Principles of Business Forecasting 2e 12.4 12.5 Marketing 449 12.4.1 New Products and Services 450 12.4.2 Long-Term Trends 451 12.4.3 Diffusion Curves: Modeling the Adoption of New Technologies and Products 12.4.4 Market Potential of a New Product or Technology 459 12.4.5 Market Response Models 464 12.5.6 Expert Adjustments and Promotional Effects 469 12.4.7 Implementation Issues 470 Forecasting Individual Behavior 470 12.5.1 Building Consumer Classification Models: The Example of Consumer Loans 12.5.2 Making Customer Relationship Management Models Effective 477 12.5.3 Appraising Models of Individual Behavior 479 12.6 Macroeconomic Forecasting 12.7 Other Applications 12.8 Forecasting Principles in Application Summary 488 Exercises 489 Minicase 12.1 Call Center Planning Minicase 12.2 Costume Jewelry Group Exercise References chapter 485 493 494 494 495 Forecasting in Practice 13 482 484 Introduction 501 502 13.1 The Process of Forecasting 502 13.1.1 The Forecasting Task 503 13.1.2 Forecasting Method Selection in Practice 505 13.1.3 Forecasting Method Selection: Which Methods Work Best Under What Circumstances? 507 13.1.4 Forecast Evaluation and Monitoring 512 13.1.5 ABC-XYZ Analysis 515 13.2 The Organization of Forecasting 518 13.2.1 The Forecaster 519 13.2.2 The Links to the Forecast User 520 13.2.3 The Politics of Forecasting 522 13.3 Dealing with Uncertainty 525 13.3.1 Simulating Uncertainty 526 13.3.2 Understanding the Impact of Uncertainty and Forecast Errors 13.3.3 Scenarios as a Means of Understanding Uncertainty 534 13.3.4 Dealing with Irreducible Uncertainty 536
13.4 Improving Forecasting 13.5 Principles for Improving Forecasting Summary 540 Exercises 541 537 539 Minicase 13.1 The Management of Call-Center Forecasting References Glossary Index 542 GLI INI The Appendices are located on the textbook companion website. appendix A appendix в appendix c Basic Statistical Concepts Forecasting Software Forecasting in R: Tutorial and Examples 541 528 454 473
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| indexdate | 2024-07-10T08:15:12Z |
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| language | English |
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| physical | xix, 544 Seiten, 23 verschieden gezählte Seiten Illustrationen, Diagramme |
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| publisher | Wessex Press, Inc. |
| record_format | marc |
| spelling | Ord, John Keith 1942- Verfasser (DE-588)13325562X aut Principles of business forecasting Keith Ord (Georgetown University), Robert Fildes (Lancaster University), Nikos Kourentzes (Lancaster University) 2nd edition New York, NY Wessex Press, Inc. [2017] xix, 544 Seiten, 23 verschieden gezählte Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Unternehmensplanung (DE-588)4078609-2 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Unternehmensplanung (DE-588)4078609-2 s Prognoseverfahren (DE-588)4358095-6 s DE-604 Fildes, Robert Verfasser (DE-588)170527492 aut Kourentzes, Nikolaos Verfasser (DE-588)1022493108 aut Erscheint auch als Online-Ausgabe 978-0-9990649-2-4 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030721182&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ord, John Keith 1942- Fildes, Robert Kourentzes, Nikolaos Principles of business forecasting Unternehmensplanung (DE-588)4078609-2 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| subject_GND | (DE-588)4078609-2 (DE-588)4358095-6 |
| title | Principles of business forecasting |
| title_auth | Principles of business forecasting |
| title_exact_search | Principles of business forecasting |
| title_full | Principles of business forecasting Keith Ord (Georgetown University), Robert Fildes (Lancaster University), Nikos Kourentzes (Lancaster University) |
| title_fullStr | Principles of business forecasting Keith Ord (Georgetown University), Robert Fildes (Lancaster University), Nikos Kourentzes (Lancaster University) |
| title_full_unstemmed | Principles of business forecasting Keith Ord (Georgetown University), Robert Fildes (Lancaster University), Nikos Kourentzes (Lancaster University) |
| title_short | Principles of business forecasting |
| title_sort | principles of business forecasting |
| topic | Unternehmensplanung (DE-588)4078609-2 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| topic_facet | Unternehmensplanung Prognoseverfahren |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030721182&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ordjohnkeith principlesofbusinessforecasting AT fildesrobert principlesofbusinessforecasting AT kourentzesnikolaos principlesofbusinessforecasting |