Forecasting with Artificial Intelligence

Edited by Mohsen Hamoudia (), Spyros Makridakis () and Evangelos Spiliotis ()

in Palgrave Advances in Economics of Innovation and Technology from Palgrave Macmillan, currently edited by Albert Link

Date: 2023
ISBN: 978-3-031-35879-1
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Chapters in this book:

Ch Chapter 1 Human Intelligence (HI) Versus Artificial Intelligence (AI) and Intelligence Augmentation (IA)

Spyros Makridakis and Antonis Polemitis

Ch Chapter 10 Forecasting Large Collections of Time Series: Feature-Based Methods

Li Li, Feng Li and Yanfei Kang

Ch Chapter 11 Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry

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

Ch Chapter 12 The Intersection of Machine Learning with Forecasting and Optimisation: Theory and Applications

Mahdi Abolghasemi

Ch Chapter 13 Enhanced Forecasting with LSTVAR-ANN Hybrid Model: Application in Monetary Policy and Inflation Forecasting

Michał Chojnowski

Ch Chapter 14 The FVA Framework for Evaluating Forecasting Performance

Michael Gilliland

Ch Chapter 2 Expecting the Future: How AI’s Potential Performance Will Shape Current Behavior

James D. Miller

Ch Chapter 3 Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future

Evangelos Spiliotis

Ch Chapter 4 Machine Learning for New Product Forecasting

Mohsen Hamoudia and Lawrence Vanston

Ch Chapter 5 Forecasting with Big Data Using Global Forecasting Models

Kasun Bandara

Ch Chapter 6 How to Leverage Data for Time Series Forecasting with Artificial Intelligence Models: Illustrations and Guidelines for Cross-Learning

Pablo Montero-Manso

Ch Chapter 7 Handling Concept Drift in Global Time Series Forecasting

Ziyi Liu, Rakshitha Godahewa, Kasun Bandara and Christoph Bergmeir

Ch Chapter 8 Neural Network Ensembles for Univariate Time Series Forecasting

Artemios-Anargyros Semenoglou, Evangelos Spiliotis and Vassilios Assimakopoulos

Ch Chapter 9 Large-Scale Time Series Forecasting with Meta-Learning

Shaohui Ma and Robert Fildes

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DOI: 10.1007/978-3-031-35879-1

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