Forecasting with Artificial Intelligence: Theory and Applications

In: Palgrave Advances in the Economics of Innovation and Technology

Abstract

Part I. Artificial intelligence : present and future -- 1. Human intelligence (HI) versus artificial intelligence (AI) and intelligence augmentation (IA) -- 2. Expecting the future: How AI's potential performance will shape current behavior -- Part II. The status of machine learning methods for time series and new products forecasting -- 3. Forecasting with statistical, machine learning, and deep learning models: Past, present and future -- 4. Machine Learning for New Product Forecasting -- Part III. Global forecasting models -- 5. Forecasting in Big Data with Global Forecasting Models -- 6. How to leverage data for Time Series Forecasting with Artificial Intelligence models: Illustrations and Guidelines for Cross-learning -- 7. Handling Concept Drift in Global Time Series Forecasting -- 8. Neural network ensembles for univariate time series forecasting -- Part IV. Meta-learning and feature-based forecasting -- 9. Large scale time series forecasting with meta-learning -- 10. Forecasting large collections of time series: feature-based methods -- Part V. Special applications -- 11. Deep Learning based Forecasting: a case study from the online fashion industry -- 12. The intersection of machine learning with forecasting and optimisation: theory and applications -- 13. Enhanced forecasting with LSTVAR-ANN hybrid model: application in monetary policy and inflation forecasting -- 14. The FVA framework for evaluating forecasting performance. .