Forecasting: theory and practice

Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne EllisonPiotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova, Florian Ziel

Research output: Contribution to journalReview articlepeer-review

267 Citations (SciVal)

Abstract

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.
Original languageEnglish
Pages (from-to)705-871
Number of pages167
JournalInternational Journal of Forecasting
Volume38
Issue number3
Early online date20 Jan 2022
DOIs
Publication statusPublished - 1 Jul 2022

Bibliographical note

Funding Information:
David F. Hendry gratefully acknowledges funding from the Robertson Foundation, USA and Nuffield College, UK .

Funding Information:
Mariangela Guidolin acknowledges the support of the University of Padua, Italy , through the grant BIRD188753/18 .

Funding Information:
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”.

Funding Information:
David T. Frazier has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414 , and ARC Early Career Researcher Award DE200101070 .

Funding Information:
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) .

Funding Information:
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.

Funding Information:
Clara Cordeiro is partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia, Portugal under the project UIDB/00006/2020 .

Funding Information:
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 ).

Funding Information:
Shari De Baets was funded by the FWO Research Foundation Flanders .

Funding Information:
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”.

Funding Information:
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 .

Funding Information:
Alessia Paccagnini acknowledges the research support by COST Action “Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry” (FinAI) CA19130 .

Funding Information:
Gael M. Martin has been supported by Australian Research Council (ARC) Discovery Grants DP170100729 and DP200101414 .

Funding Information:
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 .

Funding Information:
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].

Funding Information:
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.

Keywords

  • Applications
  • Encyclopedia
  • Methods
  • Prediction
  • Principles
  • Review
  • Time series

ASJC Scopus subject areas

  • Business and International Management

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