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El. knyga: Forecasting with Artificial Intelligence: Theory and Applications

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This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field.





The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.
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. 
Mohsen Hamoudia is CEO since 2020 of PREDICONSULT (Data and Predictive Analytics), Paris. He is a consultant to several consulting companies in Europe and the US. His research is primarily focused on economics and empirical aspects of forecasting in air transportation, telecommunications, IT (Information and Technologies), social networking, and innovation and new technologies





Spyros Makridakis is a Professor at the University of Nicosia and the founder of the Makridakis Open Forecasting Center (MOFC). He is also an Emeritus Professor at INSEAD, he joined in 1970. He has authored/co-authored, 27 books/special and more than 360 articles. He was the founding editor-in-chief of the Journal of Forecasting and the International Journal of Forecasting and is the organizer of the renowned M (Makridakis) competitions.





Evangelos Spiliotis is a Research Fellow at the Forecasting & Strategy Unit, National Technical University of Athens. Hisresearch focuses on time series forecasting with machine learning, while his work on tools for management support. He has co-organized the M4, M5, and M6 forecasting competitions.
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