Business Forecasting : The Emerging Role of Artificial Intelligence and Machine Learning

Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- State of the Art -- Forecasting in Social Settings: The State of the Art* -- I. The Facts -- A Brief History of Forecasting -- When Predictions Go Wrong -- Improving Forecasting Accuracy over Time -- The Importance of Being Uncertain -- II. What We Know -- On Explaining the Past versus Predicting the Future -- On the (Non)existence of a Best Model -- On the Performance of Machine Learning -- III. What We Are Not Sure About -- On the Prediction of Recessions/Booms/Non-stable Environments -- On the Performances of Humans versus Models -- On the Value of Explanatory Variables -- IV. What We Don't Know -- On Thin/Fat Tails and Black Swans -- On Causality -- On Luck (and Other Factors) versus Skills -- V. Conclusions -- Notes -- References -- Chapter 1 Artificial Intelligence and Machine Learning in Forecasting -- 1.1 Deep Learning for Forecasting* -- Introduction -- What Is a Neural Network? -- How Do We Forecast with Neural Nets? -- Examples of Neural Forecasting Models -- References -- 1.2 Deep Learning for Forecasting: Current Trends and Challenges* -- Applying Neural Nets as Global Forecasting Models -- Pros and Cons of Neural Forecasting -- Current Trends and Challenges -- DL Software for Forecasting -- References -- 1.3 Neural Network-Based Forecasting Strategies* -- Introduction -- Neural Network Modeling in SAS Visual Forecasting -- Modeling Strategies -- Case Study: Ozone Prediction -- Case Study: Solar Energy Forecasting -- Best Practices and Other Tips -- Conclusion -- Acknowledgments -- References -- 1.4 Will Deep and Machine Learning Solve Our Forecasting Problems?* -- Introduction -- The Good and the Bad -- The Problems -- What about the M4 Competition? -- Conclusion -- References.