Forecasting with artificial intelligence : theory and applications / Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis, editors.
Hamoudia, Mohsen, editor.; Makridakis, Spyros G., editor.; Spiliotis, Evangelos, editor.
2023
HB3730 .F67 2023
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Title
Forecasting with artificial intelligence : theory and applications / Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis, editors.
ISBN
9783031358791 electronic book
3031358791 electronic book
9783031358784
3031358783
DOI
https://doi.org/10.1007/978-3-031-35879-1
Published
Cham, Switzerland : Palgrave Macmillan, [2023]
Language
English
Description
1 online resource (xliv, 412 pages) : illustrations.
Item Number
10.1007/978-3-031-35879-1 doi
Call Number
HB3730 .F67 2023
Dewey Decimal Classification
330.01/12
Summary
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. 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.
Bibliography, etc. Note
Includes bibliographical references and indexes.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 27, 2023).
Added Author
Hamoudia, Mohsen, editor.
Makridakis, Spyros G., editor.
Spiliotis, Evangelos, editor.
Series
Palgrave advances in the economics of innovation and technology.
Available in Other Form
Forecasting with Artificial Intelligence
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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. .

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