Название: Forecasting with Artificial Intelligence: Theory and ApplicationsАвтор: Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis
Издательство: Palgrave Macmillan
Год: 2023
Страниц: 441
Язык: английский
Формат: pdf (true), epub
Размер: 22.1 MB
This book is a comprehensive guide that explores the intersection of Artificial Intelligence (AI) and forecasting, providing the latest insights and trends in this rapidly evolving field.
The evolution of forecasting methodology exhibits similar surges interspersed with periods of calm or occasional retreats. However, unlike the ocean, the field of forecasting is able to continue its progress as earlier causal and extrapolative procedures are enhanced by machine learning techniques, which is the focus of this volume.
There has been some recent concern that Artificial Intelligence (AI) is rogue science. Although most of the book focuses on the subset of AI that is Machine Learning (ML), the authors clearly embrace AI in its broadest context, to the extent of a preface written by ChatGPT. (Hey, Chat, for your information you can’t rhyme harness with harness, try farness.)
After a broad overview of AI related to forecasting, most chapters provide state-of-the-art discussions of the impact of ML upon forecasting activity relating to time series analysis, global forecasting models, large data analysis, combining forecasts, and model selection. Even newer topics such as concept drift and meta-learning sent me a-googling for definitions. The remaining chapters include case studies in economics and operations research; the finale is a chapter on Forecast Value Added (FVA), a down-to-earth method for determining whether your attempts to add value by modifying your forecasting process are making things better (or worse!).
This book explores the intersection of Artificial Intelligence (AI) and forecasting, providing an overview of the current capabilities and potential implications of the former for the theory and practice of forecasting. It contains 14 chapters that touch on various topics, such as the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, including key illustrations, state-of-the-art implementations, best practices and notable advances, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation.
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