Applications; Encyclopedia; Methods; Prediction; Principles; Review; Time series; Business and International Management; Statistics - Applications; Computer Science - Learning; econ.EM; Statistics - Machine Learning; stat.OT
Abstract :
[en] Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Precision for document type :
Review article
Disciplines :
Mathematics Business & economic sciences: Multidisciplinary, general & others Computer science
Author, co-author :
Petropoulos, Fotios; School of Management, University of Bath, United Kingdom
Apiletti, Daniele ; Politecnico di Torino, Turin, Italy
Assimakopoulos, Vassilios; Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
Babai, Mohamed Zied; Kedge Business School, France
Barrow, Devon K.; Department of Management, Birmingham Business School, University of Birmingham, United Kingdom
Ben Taieb, Souhaib; Big Data and Machine Learning Lab, université de Mons (UMONS), Belgium
Bergmeir, Christoph ; Faculty of Information Technology, Monash University, Melbourne, Australia
Bessa, Ricardo J.; INESC TEC – Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
Bijak, Jakub; Department of Social Statistics and Demography, University of Southampton, United Kingdom
Boylan, John E.; Centre for Marketing Analytics and Forecasting, Lancaster University Management School, Lancaster University, United Kingdom
Browell, Jethro; School of Mathematics and Statistics, University of Glasgow, United Kingdom
Carnevale, Claudio; Department of Mechanical and Industrial Engineering, University of Brescia, Italy
Castle, Jennifer L.; Magdalen College, University of Oxford, United Kingdom
Cirillo, Pasquale ; ZHAW School of Management and Law, Zurich University of Applied Sciences, Switzerland
Clements, Michael P. ; ICMA Centre, Henley Business School, University of Reading, United Kingdom
Cordeiro, Clara ; Faculdade de Ciências e Tecnologia, Universidade do Algarve, Portugal ; CEAUL, Faculdade de Ciências, Universidade de Lisboa, Portugal
Cyrino Oliveira, Fernando Luiz; Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
De Baets, Shari; Department of Business Informatics and Operations Management, Faculty of Economics and Business Administration, Universiteit Gent, Belgium
Dokumentov, Alexander; Let's Forecast, Australia
Ellison, Joanne; Department of Social Statistics and Demography, University of Southampton, United Kingdom
Fiszeder, Piotr ; Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Poland
Franses, Philip Hans; Econometric Institute, Erasmus School of Economics, Rotterdam, Netherlands
Frazier, David T.; Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
Gilliland, Michael; SAS, United States
Gönül, M. Sinan ; Newcastle Business School, Northumbria University, United Kingdom
Goodwin, Paul; School of Management, University of Bath, United Kingdom
Grossi, Luigi ; Department of Statistical Sciences, University of Padua, Italy
Grushka-Cockayne, Yael; Darden School of Business, University of Virginia, United States
Guidolin, Mariangela ; Department of Statistical Sciences, University of Padua, Italy
Guidolin, Massimo; Finance Department, Bocconi University and Baffi-CAREFIN Centre, Milan, Italy
Gunter, Ulrich; Department of Tourism and Service Management, MODUL University Vienna, Austria
Guo, Xiaojia ; Robert H. Smith School of Business, University of Maryland, United States
Guseo, Renato ; Department of Statistical Sciences, University of Padua, Italy
Harvey, Nigel; Department of Experimental Psychology, University College London, United Kingdom
Hendry, David F. ; Nuffield College and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, United Kingdom
Hollyman, Ross; School of Management, University of Bath, United Kingdom
Januschowski, Tim; Amazon Research, Germany
Jeon, Jooyoung; Korea Advanced Institute of Science and Technology, South Korea
Jose, Victor Richmond R.; McDonough School of Business, Georgetown University, United States
Kang, Yanfei ; School of Economics and Management, Beihang University, Beijing, China
Koehler, Anne B.; Miami University, United States
Kolassa, Stephan; Centre for Marketing Analytics and Forecasting, Lancaster University Management School, Lancaster University, United Kingdom ; SAP, Switzerland
Kourentzes, Nikolaos ; Centre for Marketing Analytics and Forecasting, Lancaster University Management School, Lancaster University, United Kingdom ; Skövde Artificial Intelligence Lab, School of Informatics, University of Skövde, Sweden
Leva, Sonia; Department of Energy, Politecnico di Milano, Italy
Li, Feng ; School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
Litsiou, Konstantia; Manchester Metropolitan University Business School, United Kingdom
Makridakis, Spyros; M Open Forecasting Center & Institute for the Future, University of Nicosia, Nicosia, Cyprus
Martin, Gael M.; Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
Martinez, Andrew B. ; Office of Macroeconomic Analysis, US Department of the Treasury, Washington, DC, United States ; GWU Research Program on Forecasting, Washington, DC, United States
Meeran, Sheik; School of Management, University of Bath, United Kingdom
David F. Hendry gratefully acknowledges funding from the Robertson Foundation, USA and Nuffield College, UK .Mariangela Guidolin acknowledges the support of the University of Padua, Italy , through the grant BIRD188753/18 .Piotr Fiszeder was supported by the National Science Centre, Poland project number 2016/21/B/HS4/00662 entitled “Multivariate volatility models - the application of low and high prices”.David T. Frazier has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414 , and ARC Early Career Researcher Award DE200101070 .Joanne Ellison acknowledges the support of the ESRC FertilityTrends project (grant number ES/S009477/1) and the ESRC Centre for Population Change (grant number ES/R009139/1) .Fotios Petropoulos would like to thank all the co-authors of this article for their very enthusiastic response and participation in this initiave. He would also like to thank Pierre Pinson for inviting this paper to be submitted to the International Journal of Forecasting. The constructive comments and suggestions from this advisory board were vital in improving the paper. He also thanks Artur Tarassow for offering a list of Gretl's software functionalities. Jakub Bijak's work received funding from the European Union's Horizon 2020 research and innovation programme, grant 870299 QuantMig: Quantifying Migration Scenarios for Better Policy. Clara Cordeiro is partially financed by national funds through FCT ? Funda??o para a Ci?ncia e a Tecnologia, Portugal under the project UIDB/00006/2020. Fernando Luiz Cyrino Oliveira acknowledges the support of the Coordination for the Improvement of Higher Level Personnel (CAPES), Brazil ? grant number 001, the Brazilian National Council for Scientific and Technological Development (CNPq) ? grant number 307403/2019-0, and the Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ) ? grant numbers 202.673/2018 and 211.086/2019. Shari De Baets was funded by the FWO Research Foundation Flanders. Joanne Ellison acknowledges the support of the ESRC FertilityTrends project (grant number ES/S009477/1) and the ESRC Centre for Population Change (grant number ES/R009139/1). Piotr Fiszeder was supported by the National Science Centre, Poland project number 2016/21/B/HS4/00662 entitled ?Multivariate volatility models - the application of low and high prices?. David T. Frazier has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414, and ARC Early Career Researcher AwardDE200101070. Mariangela Guidolin acknowledges the support of the University of Padua, Italy, through the grant BIRD188753/18. David F. Hendry gratefully acknowledges funding from the Robertson Foundation, USA and Nuffield College, UK. Yanfei Kang acknowledges the support of the National Natural Science Foundation of China (number 11701022) and the National Key Research and Development Program, China (number 2019YFB1404600). Stephan Kolassa would like to thank Tilmann Gneiting for some very helpful tips. Gael M. Martin has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414. Alessia Paccagnini acknowledges the research support by COST Action ?Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry? (FinAI)CA19130. Jose M. Pav?a acknowledges the support of the Spanish Ministry of Science, Innovation and Universities and the Spanish Agency of Research, co-funded with FEDER funds, grant ECO2017-87245-R, and of Conseller?a d'Innovaci?, Universitats, Ci?ncia i Societat Digital, Generalitat Valenciana ? grant number AICO/2019/053. Diego J. Pedregal and Juan Ramon Trapero Arenas acknowledge the support of the European Regional Development Fund and Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) under the project SBPLY/19/180501/000151 and by the Vicerrectorado de Investigaci?n y Pol?tica Cient?fica from UCLM, Spain through the research group fund program PREDILAB; DOCM 26/02/2020 [2020-GRIN-28770]. David E. Rapach thanks Ilias Filippou and Guofu Zhou for valuable comments. J. James Reade and Han Lin Shang acknowledge Shixuan Wang for his constructive comments. Micha? Rubaszek is thankful for the financial support provided by the National Science Centre, Poland, grant No. 2019/33/B/HS4/01923 entitled ?Predictive content of equilibrium exchange rate models?. The views expressed in this paper are those of the authors and do not necessarily reflect the views of their affiliated institutions and organisations.Clara Cordeiro is partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia, Portugal under the project UIDB/00006/2020 .Yanfei Kang acknowledges the support of the National Natural Science Foundation of China (number 11701022 ) and the National Key Research and Development Program, China (number 2019YFB1404600 ).Shari De Baets was funded by the FWO Research Foundation Flanders .Michał Rubaszek is thankful for the financial support provided by the National Science Centre, Poland , grant No. 2019/33/B/HS4/01923 entitled “Predictive content of equilibrium exchange rate models”.Jose M. Pavía acknowledges the support of the Spanish Ministry of Science, Innovation and Universities and the Spanish Agency of Research, co-funded with FEDER funds , grant ECO2017-87245-R , and of Consellería d’Innovació, Universitats, Ciència i Societat Digital, Generalitat Valenciana – grant number AICO/2019/053 .Alessia Paccagnini acknowledges the research support by COST Action “Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry” (FinAI) CA19130 .Gael M. Martin has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414 .Fernando Luiz Cyrino Oliveira acknowledges the support of the Coordination for the Improvement of Higher Level Personnel (CAPES), Brazil – grant number 001 , the Brazilian National Council for Scientific and Technological Development (CNPq) – grant number 307403/2019-0 , and the Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ) – grant numbers 202.673/2018 and 211.086/2019 .Diego J. Pedregal and Juan Ramon Trapero Arenas acknowledge the support of the European Regional Development Fund and Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) under the project SBPLY/19/180501/000151 and by the Vicerrectorado de Investigación Política Científica from UCLM, Spain through the research group fund program PREDILAB; DOCM 26/02/2020 [2020-GRIN-28770].Jakub Bijak’s work received funding from the European Union’s Horizon 2020 research and innovation programme , grant 870299 QuantMig: Quantifying Migration Scenarios for Better Policy.
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