Forecasting with Artificial Intelligence

1. Front Matter

   Pages i-xliv

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2. Artificial Intelligence: Present and Future

   1. Front Matter

      Pages 1-1

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   2. Human Intelligence (HI) Versus Artificial Intelligence (AI) and Intelligence Augmentation (IA)

      • Spyros Makridakis, Antonis Polemitis

      Pages 3-29

   3. Expecting the Future: How AI’s Potential Performance Will Shape Current Behavior

      • James D. Miller

      Pages 31-45

3. The Status of Machine Learning Methods for Time Series and New Product Forecasting

   1. Front Matter

      Pages 47-47

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   2. Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future

      • Evangelos Spiliotis

      Pages 49-75

   3. Machine Learning for New Product Forecasting

      • Mohsen Hamoudia, Lawrence Vanston

      Pages 77-104

4. Global Forecasting Models

   1. Front Matter

      Pages 105-105

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   2. Forecasting with Big Data Using Global Forecasting Models

      • Kasun Bandara

      Pages 107-122

   3. How to Leverage Data for Time Series Forecasting with Artificial Intelligence Models: Illustrations and Guidelines for Cross-Learning

      • Pablo Montero-Manso

      Pages 123-162

   4. Handling Concept Drift in Global Time Series Forecasting

      • Ziyi Liu, Rakshitha Godahewa, Kasun Bandara, Christoph Bergmeir

      Pages 163-189

   5. Neural Network Ensembles for Univariate Time Series Forecasting

      • Artemios-Anargyros Semenoglou, Evangelos Spiliotis, Vassilios Assimakopoulos

      Pages 191-218

5. Meta-Learning and Feature-Based Forecasting

   1. Front Matter

      Pages 219-219

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   2. Large-Scale Time Series Forecasting with Meta-Learning

      • Shaohui Ma, Robert Fildes

      Pages 221-250

   3. Forecasting Large Collections of Time Series: Feature-Based Methods

      • Li Li, Feng Li, Yanfei Kang

      Pages 251-276

6. Special Applications

   1. Front Matter

      Pages 277-277

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   2. Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry

      • Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Tim Januschowski

      Pages 279-311

   3. The Intersection of Machine Learning with Forecasting and Optimisation: Theory and Applications

      • Mahdi Abolghasemi

      Pages 313-339

   4. Enhanced Forecasting with LSTVAR-ANN Hybrid Model: Application in Monetary Policy and Inflation Forecasting

      • Michał Chojnowski

      Pages 341-372

   5. The FVA Framework for Evaluating Forecasting Performance

      • Michael Gilliland

      Pages 373-384

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.

• Artificial Intelligence
• Machine Learning
• Forecasting
• Predictive Analysis
• Data Science
• Data Mining
• Finance
• Energy
• Supply-Chain Management
• Algorithm

• France Telecom Group, Orange Business Services, Eragny, France

  Mohsen Hamoudia

• Institute For the Future (IFF), University of Nicosia, Engomi, Cyprus

  Spyros Makridakis

• School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece

  Evangelos Spiliotis

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. His research 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.