東華大學圖書館 |

Forecasting with artificial intelligence = theory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Forecasting with artificial intelligence/ edited by Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis.
其他題名:	theory and applications /
其他作者:	Hamoudia, Mohsen.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xliv, 412 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Economic forecasting. -
電子資源:	https://doi.org/10.1007/978-3-031-35879-1
ISBN:	9783031358791

Forecasting with artificial intelligence = theory and applications /
Forecasting with artificial intelligencetheory and applications /[electronic resource] :edited by Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis. - Cham :Springer Nature Switzerland :2023. - xliv, 412 p. :ill., digital ;24 cm. - Palgrave advances in the economics of innovation and technology,2662-3870. - Palgrave advances in the economics of innovation and technology..

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.

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.

ISBN: 9783031358791

Standard No.: 10.1007/978-3-031-35879-1doiSubjects--Topical Terms:

527353
Economic forecasting.

LC Class. No.: HB3730 / .F67 2023

Dewey Class. No.: 330.0112

Forecasting with artificial intelligence = theory and applications /
LDR:04670nmm a2200337 a 4500 001 2334617
003 DE-He213
005 20230920125523.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031358791 $q (electronic bk.)
020 $a 9783031358784 $q (paper)
024 7 $a 10.1007/978-3-031-35879-1 $2 doi
035 $a 978-3-031-35879-1
040 $a GP $c GP
041 0 $a eng
050 4 $a HB3730 $b .F67 2023
072 7 $a KC $2 bicssc
072 7 $a BUS000000 $2 bisacsh
072 7 $a KC $2 thema
082 0 4 $a 330.0112 $2 23
090 $a HB3730 $b .F714 2023
245 0 0 $a Forecasting with artificial intelligence $h [electronic resource] : $b theory and applications / $c edited by Mohsen Hamoudia, Spyros Makridakis, Evangelos Spiliotis.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Palgrave Macmillan, $c 2023.
300 $a xliv, 412 p. : $b ill., digital ; $c 24 cm.
490 1 $a Palgrave advances in the economics of innovation and technology, $x 2662-3870
505 0 $a 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.
520 $a 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.
650 0 $a Economic forecasting. $3 527353
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Economics of Innovation. $3 3591766
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Theory and Algorithms for Application Domains. $3 3594704
700 1 $a Hamoudia, Mohsen. $3 3524452
700 1 $a Makridakis, Spyros G. $3 657308
700 1 $a Spiliotis, Evangelos. $3 3666380
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Palgrave advances in the economics of innovation and technology. $3 3200812
856 4 0 $u https://doi.org/10.1007/978-3-031-35879-1
950 $a Economics and Finance (SpringerNature-41170)