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001481164 020__ $$a9783031358791$$qelectronic book
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001481164 0247_ $$a10.1007/978-3-031-35879-1$$2doi
001481164 035__ $$aSP(OCoLC)1399970744
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001481164 050_4 $$aHB3730$$b.F67 2023
001481164 08204 $$a330.01/12$$223/eng/20230927
001481164 24500 $$aForecasting with artificial intelligence :$$btheory and applications /$$cMohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis, editors.
001481164 264_1 $$aCham, Switzerland :$$bPalgrave Macmillan,$$c[2023]
001481164 300__ $$a1 online resource (xliv, 412 pages) :$$billustrations.
001481164 336__ $$atext$$btxt$$2rdacontent
001481164 337__ $$acomputer$$bc$$2rdamedia
001481164 338__ $$aonline resource$$bcr$$2rdacarrier
001481164 4901_ $$aPalgrave Advances in the Economics of Innovation and Technology.
001481164 504__ $$aIncludes bibliographical references and indexes.
001481164 5050_ $$aPart I. Artificial intelligence : present and future -- 1. Human intelligence (HI) versus artificial intelligence (AI) and intelligence augmentation (IA) -- 2. Expecting the future: How AI's potential performance will shape current behavior -- Part II. The status of machine learning methods for time series and new products forecasting -- 3. Forecasting with statistical, machine learning, and deep learning models: Past, present and future -- 4. Machine Learning for New Product Forecasting -- Part III. Global forecasting models -- 5. Forecasting in Big Data with Global Forecasting Models -- 6. How to leverage data for Time Series Forecasting with Artificial Intelligence models: Illustrations and Guidelines for Cross-learning -- 7. Handling Concept Drift in Global Time Series Forecasting -- 8. Neural network ensembles for univariate time series forecasting -- Part IV. Meta-learning and feature-based forecasting -- 9. Large scale time series forecasting with meta-learning -- 10. Forecasting large collections of time series: feature-based methods -- Part V. Special applications -- 11. Deep Learning based Forecasting: a case study from the online fashion industry -- 12. The intersection of machine learning with forecasting and optimisation: theory and applications -- 13. Enhanced forecasting with LSTVAR-ANN hybrid model: application in monetary policy and inflation forecasting -- 14. The FVA framework for evaluating forecasting performance. .
001481164 506__ $$aAccess limited to authorized users.
001481164 520__ $$aThis 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.
001481164 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 27, 2023).
001481164 650_0 $$aEconomic forecasting.$$zChina$$0(DLC)sh2008102568
001481164 650_0 $$aArtificial intelligence.$$xMedical applications$$0(DLC)sh 88003000
001481164 655_0 $$aElectronic books.
001481164 7001_ $$aHamoudia, Mohsen,$$eeditor.
001481164 7001_ $$aMakridakis, Spyros G.,$$eeditor.$$0(OCoLC)oca00352985
001481164 7001_ $$aSpiliotis, Evangelos,$$eeditor.
001481164 77608 $$iPrint version:$$aHamoudia, Mohsen$$tForecasting with Artificial Intelligence$$dCham : Palgrave Macmillan,c2023$$z9783031358784
001481164 830_0 $$aPalgrave advances in the economics of innovation and technology.
001481164 852__ $$bebk
001481164 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35879-1$$zOnline Access$$91397441.1
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001481164 982__ $$aEbook
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