Forecasting & Predictive Analytics. with ForecastX. Seventh Edition. John Galt Solutions, Inc. Chicago

Transcription

1 Forecasting & Predictive Analytics with ForecastX Seventh Edition Barry Keating University ofnotre Dame J. Holton Wilson Central Michigan University John Galt Solutions, Inc. Chicago Boston Burr Ridge, IL Dubuque, IA New York San Francisco St. Louis Bangkok Bogota Caracas Kuala Lumpur Lisbon London Madrid Mexico City Milan Montreal New Delhi Santiago Seoul Singapore Sydney Taipei Toronto Mc Graw Hill Education

2 Contents Chapter 1 Introduction to Business Forecasting and Predictive Analytics 1 Introduction 1 Forecasting Is Essential for Success in Business 2 S:ages in the Development of Business Forecasting 2 The Structure ofthis Text 3 Section 1: Time Series Models 4 Section 2: Demand Planning 5 Section 3; Analytics 5 Quantitative Forecasting Has Become Widely Accepted 6 Forecasting in Business Today 7 ( omments from the Field: Post Foods 9 Comments from the Field: Petsafe 9 Gommerns from the Field: Global Forecasting Lssues, Ocean Spray Cranberries 10 Forecasting in the Public and Not-for-Profit Sectors 10 A Po!ic<: Department 10 The Texas Legislative Board II The California Legislative Analysis Office 11 Forecasting and Supply Chain Management 12 Collaborative Forecasting 14 Computer Use and Quantitative Forecasting 15 Qualitative or Subjective Forecasting Methods 16 Sah's Force Composites 16 Surveys of Customers and the General Population 16 Jury of Executive Opinion 17 The Delphi Method 17 Some Advantages and Disadvantages of Subjective Methods 18 New-Product Forecasting 18 Using Marketing Research to Aid New-Product Forecasting 19 The Product Life Cycle Concept Aids in New-Product Forecasting 20 Analog Forecasts 21 Test Marketing 21 Product Clinics 22 Type of Product Affects New-Product Forecasting 22 The Bass Model for New-Product Forecasting 22 Forecasting Sales for New Products That Have Short Product Life Cycles 24 A Simple Naive Forecasting Model 27 Evaluating Forecasts 29 Using Multiple Forecasts 32 Sources of Data 32 Forecasting Total New Houses Sold 33 Steps to Better Time Series Forecasts 34 Integrative Case: Forecasting Sales of the Gap 35 Background of the Gap and Gap Sales 35 An Introduction to ForecastX 39 Forecasting with the ForecastX Wizard 39 Using the Five Main Tabs on the Opening ForecastX Screen 39 Suggested Readings and Web Sites 44 Exercises 44 Chapter 2 The Forecast Process, Data Considerations, and Model Selection 47 Introduction 47 The Forecast Process 48 Trend, Seasonal, and Cyclical Data Pattems 51 Data Pattems and Model Selection 54 A Statistical Review 56 Descriptive Statistics 56 The Normal Distribution 60 The Student's t-distribution 65 From Sample to Population: Statistical Inference 67 Hypothesis Testing 68 Correlation 73 xv

3 xvl Contents Correlograms: Another Method of Data Exploration 76 Total New Houses Sold: Exploratory Data Analysis and Model Selection 79 Business Forecasting: A Process, Not an Application 80 Integrative Case: The Gap 81 Comments from the Field: Anchorage Economic Development Center Secures Time-Saving Forecasting Accuracy 83 Using ForecastX to Find Autocorrelation Functions 84 Suggested Readings 86 Exercises 87 Chapter 3 Extrapolation 1. Moving Averages and Exponential Smoothing 92 Moving Averages 93 Simple Exponential Smoothing 100 Holt's Exponential Smoothing 105 Winters' Exponential Smoothing 111 The Seasonal Indices 114 Adaptive-Response-Rate Single Exponential Smoothing 115 Using Single, Holt's, or ADRES Smoothing to Forecast a Seasonal Data Series 118 New-Product Forecasting (Growth Curve Eitting) 121 Gompertz Curve 125 Logistics Curve 128 Boss Model 131 The Bass Model in Action 132 Event Modeling 135 Forecasting Jewelry Sales with Exponential Smoothing 140 Summary 142 Integrative Case: The Gap 143 Using ForecastX to Make Exponential Smoothing Forecasts 145 Suggested Readings 150 Exercises 150 Chapter 4 Extrapolation 2. Introduction to Forecasting with Regression Trend Models 159 The Bivariate Regression Model 160 Visualization of Data: An Important Step in Regression Analysis 161 A Process for Regression Forecasting 163 Forecasting with a Simple Linear Trend 164 Using a Causal Regression Model to Forecast 169 A Jewelry Sales Forecast Based on Disposable Personal Income 171 Statistical Evaluation of Regression Models 17 8 Basic Diagnostic Checks for Evaluating Regression Results 178 Using the Standard Error of the Estimate 183 Serial Correlation 185 Heteroscedasticity 190 Cross-Sectional Forecasting 191 Forecasting Total Houses Sold with Two Bivariate Regression Models 193 Comments from the Field 198 Integrative Case: The Gap 199 Using ForecastX to Make Regression Forecasts 201 Further Comments on Using ForecastX to Develop Regression Models 204 Suggested Readings 208 Exercises 208 Chapter 5 Explanatory Models 1. Forecasting with Multiple Regression Causal Models 220 The Multiple-Regression Model 221 Initial Considerations When Selecting Independent Variables 222 Developing Multiple-Regression Models 223 A Three-Dimensional Scattergram 225 Statistical Evaluation of Multiple-Regression Models 227 The First Four Quick Checks in Evaluating Multiple-Regression Models 228

4 Contents xvii MulticolUnearity 234 Serial Correlation: An Extended Look 236 Serial Correlation and the Omitted-Variable Problem 237 Alternative-Variable Selection Criteria 240 Accounting for Seasonality in a Multiple- Regression Model 242 Using a Dummy Variable to Account for a Recession 248 Extensions of the Multiple-Regression Model 251 Advice on Using Multiple Regression in Forecasting 256 Independent Variable Selection 257 The Gap Example (With Leading Indicators) 259 Integrative Case: The Gap 265 Using ForecastX to Make Multiple-Regression Forecasts 269 Suggested Readings 272 Exercises 273 Appendix: Combining Forecasts (Ensemble Models) 278 Introduction 278 Blas 279 What Kinds of Forecasts Can be Combined? 281 Considerations in Choosing the Weights for Cornbined Forecasts 282 One Technique for Selecting Weights When Combining Forecasts 283 An Application ofthe Regression Method for Combining Forecasts 284 Summarizing the Steps for Combining Forecasts 287 Integrative Case: The Gap 289 Using ForecastX to Combine Forecasts 291 Suggested Readings 298 Exercises 300 Chapter 6 Explanatory Models 2. Time-Series Decomposition 302 The Basic Time-Series Decomposition Model 303 Deseasonalizing the Data and Finding Seasonal Indices 306 Finding the Long-Term Trend 311 Measuring the Cyclical Component 312 Overview of Business Cycles 313 Business Cycle Indicators 314 The Cycle Factor for Private Housing Starts 315 The Time-Series Decomposition Forecast 318 Forecasting Winter Daily Natural Gas Demand at Vermont Gas Systems 321 Integrative Case: The Gap 321 Using ForecastX to Make Time-Series Decomposition Forecasts 326 Suggested Readings 329 Exercises 330 Chapter 7 Explanatory Models 3. ARIMA (Box- Jenkins) Forecasting Models 336 Introduction 336 The Philosophy of Box-Jenkins 337 Moving-Average Models 339 Autoregressive Models 346 Mixed Autoregressive and Moving-Average Models 348 Stationarity 351 The Box-Jenkins Identification Process 355 ARIMA: A Set of Numerical Examples 360 Example Example Example Example Forecasting Seasonal Time Series 372 Total Houses Sold 372 ARIMA in Actual Use: Intelligent Transportation Systems 375 Integrative Case: Forecasting Sales of the Gap 376 Overfitting 379 Using ForecastX to Make ARIMA (Box-Jenkins) Forecasts 380 Suggested Readings 382 Exercises 383 Appendix: Critical Values of Chi-Square 392

5 xviii Contents Chapter 8 Predictive Analytics: Helping to Make Sense of Big Data 394 Applying Analytics in Financial Institutions' Fight Against Fraud 395 Introduction 399 Big Data 400 Analytics 401 Big Data and Iis Characteristics 402 "Dataßcation" 405 Data Mining 406 Dutabuse Management 407 Data Mining Versus Database Management 407 Pattems in Data Mining 409 The Tools of Analytics 409 Statistical Forecasting and Data Mining 411 Terminology in Data Mining: Speak Like a Data Miner 411 Correlation 412 F.ariy Uses of A nalytics 413 The "Steps" in a Data Mining Process 415 The Data Itself 416 Overfitting 417 Accuracy and Fit (Agaiii) 418 Some Other Data Considerations 418 Sampling/Partitioning 420 Diagnostics (Evaluating Predictive Performance) 421 The Confusion Matrix and Misclassification Rate 421 The Lift Chart 423 The Receiver Operating Curve (ROC) and Area Under the Curve (AUC) 426 What Is to Follow 427 Suggested Readings 427 Exercises 428 Chapter 9 Classification Models: The Most Used Models in Analytics 430 Introduction 430 A Data Mining Classification Example: k-nearest-neighbor (knn) 434 A Business Data Mining Classification Example: k-nearest-neighbor (knn) 438 Classification Trees: A Second Classification Technique 445 A Business Data Mining Example: Classification Trees 449 A Business Data Mining Example: Regression Trees 452 Naive Bayes: A Third Classification Technique 454 Logit: A Fourth Classification Technique 463 Bank Distress 467 Summary 472 Suggested Readings 472 Exercises 473 Chapter 10 Ensemble Models and Clustering 477 Introduction 477 Ensembles 478 Error Due to Bias 478 Error Due to Variance 479 The Case for Boosting and Bagging 480 Bagging 481 Boosting 486 Random Forest 487 A Bagging Example 487 A Boosting Example 490 A Random Forest (i.e., Tree) Example 491 Clustering 493 Usefulness of Clustering 495 How Clustering Works 496 A Clustering Example (k-means Clustering) 497 A Hierarchical Clustering Example (Agglomerative Bottom-Up Clustering) 500 Suggested Readings and Web Sites 503 Exercises 504 Chapter 11 Text Mining 506 Introduction 506 Why Turn Text into Numbers? 507 Where to Start The "Bag of Words" Analysis 507 Newsgroups 510

6 Contents xix in Pictures 514 Understanding SVD 515 Back to the Usenet Example 516 A Logistics Regression Classification of the Usenet Postings 520 Natural Language Processing 521 Data Mining and Text Mining Combi ned 524 Target Leakage 529 Conciusion 529 Suggested Readings 530 Exercises 531 Chapter 12 Forecast/Analytics Implementation 533 Forecasting Involves a Definite Flow 534 The Forecast Process Ninc-Step Forecasting Process 537 Su p i. Spccify Objectives Determine What to Forecast 538 Siep 3. Identify Time Dimensions 538 Step 4. Data Considerations 538 Step 5. Model Selection 539 Step 6. Model Evaluation 539 Step 7. Forecast Preparation 540 Step 8. Forecast Presentation 540 Step 9. Tracking Results 541 Choosing a Forecasting Technique 542 Sales Force Composite (SFC) 543 Customer Surveys (CS) 543 Jury of Executive Opinion (JEO) 545 Delphi Method 545 Naive 545 Moving Averages 545 Simple Exponential Smoothing (SES) 545 Adaptive-Response-Rate Single Exponential Smoothing (ADRES) 546 Holt's Exponential Smoothing (HES) 546 Winters' Exponential Smoothing (WES) 546 Regression-Based Trend Models 546 Regression-Based Trend Models with Seasonality 546 Regression Models with Causality 547 Time-Series Decomposition (TSD) 547 ARIMA 547 Data Mining 548 Text Mining 548 Special Forecasting Considerations 548 Event Modeling 549 Combining Forecasts (Ensembles) 549 New-Product Forecasting (NPF) 550 Data Mining 550 Text Mining 551 Using Procast in ForecastX to Make Forecasts 552 Suggested Readings 555 Exercises 556 Index 557