Foreword (Spyros Makridakis and Fotios Petropoulos) xi -- Preface (Michael Gilliland, Len Tashman, and Udo Sglavo) xv -- State of the Art 1 -- Forecasting in Social Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Fotios Petropoulos) 1 -- Chapter 1 Artificial Intelligence and Machine Learning in Forecasting 31 -- 1.1 Deep Learning for Forecasting (Tim Januschowski and colleagues) 32 -- 1.2 Deep Learning for Forecasting: Current Trends and Challenges (Tim Januschowski and Colleagues) 41 -- 1.3 Neural Network--Based Forecasting Strategies (Steven Mills and Susan Kahler) 48 -- 1.4 Will Deep and Machine Learning Solve Our Forecasting Problems? (Stephan Kolassa) 65 -- 1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future (Spyros Makridakis) 72 -- Commentary: Spyros Makridakis's Article "Forecasting The Impact Of Artificial Intelligence" (Owen Davies) 80 -- 1.6 Forecasting the Impact of Artificial Intelligence: Another Voice (Lawrence Vanston) 84 -- Commentary: Response to Lawrence Vanston (Spyros Makridakis) 92 -- 1.7 Smarter Supply Chains through AI (Duncan Klett) 94 -- 1.8 Continual Learning: The Next Generation of Artificial Intelligence (Daniel Philps) 103 -- 1.9 Assisted Demand Planning Using Machine Learning (Charles Chase) 110 -- 1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics (Jeff Baker) 115 -- 1.11 The M4 Forecasting Competition -- Takeaways for the Practitioner (Michael Gilliland) 124 -- Commentary --The M4 Competition and a Look to the Future (Fotios Petropoulos) 132 -- Chapter 2 Big Data in Forecasting 135 -- 2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting? (Shaun Snapp) 136 -- Commentary: Becoming Responsible Consumers of Big Data (Chris Gray) 142 -- Commentary: Customer versus Item Forecasting (Michael Gilliland) 146 -- Commentary: Big Data or Big Hype? (Stephan Kolassa) 148 -- Commentary: Big Data, a Big Decision (Niels van Hove) 150 -- Commentary: Big Data and the Internet of Things (Peter Catt) 152 -- 2.2 How Big Data Could Challenge Planning Processes across the Supply Chain (Tonya Boone, Ram Ganeshan, and Nada Sanders) 155 -- Chapter 3 Forecasting Methods: Modeling, Selection, and Monitoring 163 -- 3.1 Know Your Time Series (Stephan Kolassa and Enno Siemsen) 164 -- 3.2 A Classification of Business Forecasting Problems (Tim Januschowski and Stephan Kolassa) 171 -- 3.3 Judgmental Model Selection (Fotios Petropoulos) 181 -- Commentary: A Surprisingly Useful Role for Judgment (Paul Goodwin) 192 -- Commentary: Algorithmic Aversion and Judgmental Wisdom (Nigel Harvey) 194 -- Commentary: Model Selection in Forecasting Software (Eric Stellwagen) 195 -- Commentary: Exploit Information from the M4 Competition (Spyros Makridakis) 197 -- 3.4 A Judgment on Judgment (Paul Goodwin) 198 -- 3.5 Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) 207 -- 3.6 A Primer on Probabilistic Demand Planning (Stefan de Kok) 211 -- 3.7 Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) 215 -- 3.8 Get Your CoV On . . . (Lora Cecere) 225 -- 3.9 Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) 230 -- 3.10 Monitoring Forecast Models Using Control Charts (Joe Katz) 232 -- 3.11 Forecasting the Future of Retail Forecasting (Stephan Kolassa) 243 Commentary (Brian Seaman) 255 -- Chapter 4 Forecasting Performance 259 -- 4.1 Using Error Analysis to Improve Forecast Performance (Steve Morlidge) 260 -- 4.2 Guidelines for Selecting a Forecast Metric (Patrick Bower) 271 -- 4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error (Stefan de Kok) 277 -- 4.4 Beware of Standard Prediction Intervals from Causal Models (Len Tashman) 290 -- Chapter 5 Forecasting Process: Communication, Accountability, and S&OP 297 -- 5.1 Not Storytellers But Reporters (Steve Morlidge) 298 -- 5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? (Chris Gray) 303 -- 5.3 Communicating the Forecast: Providing Decision Makers with Insights (Alec Finney) 310 -- 5.4 An S&OP Communication Plan: The Final Step in Support of Company Strategy (Niels van Hove) 317 -- 5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide (Todd Tomalak) 325 -- 5.6 How to Identify and Communicate Downturns in Your Business (Larry Lapide) 331 -- 5.7 Common S&OP Change Management Pitfalls to Avoid (Patrick Bower) 338 -- 5.8 Five Steps to Lean Demand Planning (John Hellriegel) 342 -- 5.9 The Move to Defensive Business Forecasting (Michael Gilliland) 346 -- Afterwords: Essays on Topics in Business Forecasting 351 -- Observations from a Career Practitioner: Keys to Forecasting Success (Carolyn Allmon) 351 -- Demand Planning as a Career (Jason Breault) 354 -- How Did We Get Demand Planning So Wrong? (Lora Cecere) 357 -- Business Forecasting: Issues, Current State, and Future Direction (Simon Clarke) 358 -- Statistical Algorithms, Judgment and Forecasting Software Systems (Robert Fildes) 361 -- The <> for Forecasting (Igor Gusakov) 364 -- The Future of Forecasting Is Artificial Intelligence Combined with Human Forecasters (Jim Hoover) 367 -- Quantile Forecasting with Ensembles and Combinations (Rob J. Hyndman) 371 -- Managing Demand for New Products (Chaman L. Jain) 376 -- Solving for the Irrational: Why Behavioral Economics Is the Next Big Idea in Demand Planning (Jonathon Karelse) 380 -- Business Forecasting in Developing Countries (Bahman Rostami-Tabar) 382 -- Do the Principles of Analytics Apply to Forecasting? (Udo Sglavo) 387 -- Groupthink on the Topic of AI/ML for Forecasting (Shaun Snapp) 390 -- Taking Demand Planning Skills to the Next Level (Nicolas Vandeput) 392 -- Unlock the Potential of Business Forecasting (Eric Wilson) 394 -- Building a Demand Plan Story for S&OP: The Business Value of Analytics (Dr. Davis Wu) 396 -- About the Editors 401 -- Index 403.