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 Ellison; Piotr 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

doi:10.1016/j.ijforecast.2021.11.001

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

^*Corresponding author for this work

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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 language	English
Pages (from-to)	705-871
Number of pages	167
Journal	International Journal of Forecasting
Volume	38
Issue number	3
Early online date	20 Jan 2022
DOIs	https://doi.org/10.1016/j.ijforecast.2021.11.001
Publication status	Published - Jul 2022

Bibliographical note

Acknowledgments:
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 – Fundação para a Ciência 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 Award DE200101070.

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. 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.

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].

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.

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”.

Keywords

Review
Encyclopedia
Methods
Applications
Principles
Time series
Prediction

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PetropoulosF2022ForecastingFinal published version, 3.81 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., ... Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001

@article{c68eb3bdadfc4ca78e834e9b127a5010,

title = "Forecasting: theory and practice",

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.",

keywords = "Review, Encyclopedia, Methods, Applications, Principles, Time series, Prediction",

author = "Fotios Petropoulos and Daniele Apiletti and Vassilios Assimakopoulos and Babai, {Mohamed Zied} and Barrow, {Devon K.} and {Ben taieb}, Souhaib and Christoph Bergmeir and Bessa, {Ricardo J.} and Jakub Bijak and Boylan, {John E.} and Jethro Browell and Claudio Carnevale and Castle, {Jennifer L.} and Pasquale Cirillo and Clements, {Michael P.} and Clara Cordeiro and {Cyrino Oliveira}, {Fernando Luiz} and {De Baets}, Shari and Alexander Dokumentov and Joanne Ellison and Piotr Fiszeder and Franses, {Philip Hans} and Frazier, {David T.} and Michael Gilliland and G{\"o}n{\"u}l, {M. Sinan} and Paul Goodwin and Luigi Grossi and Yael Grushka-Cockayne and Mariangela Guidolin and Massimo Guidolin and Ulrich Gunter and Xiaojia Guo and Renato Guseo and Nigel Harvey and Hendry, {David F.} and Ross Hollyman and Tim Januschowski and Jooyoung Jeon and Jose, {Victor Richmond R.} and Yanfei Kang and Koehler, {Anne B.} and Stephan Kolassa and Nikolaos Kourentzes and Sonia Leva and Feng Li and Konstantia Litsiou and Spyros Makridakis and Martin, {Gael M.} and Martinez, {Andrew B.} and Sheik Meeran and Theodore Modis and Konstantinos Nikolopoulos and Dilek {\"O}nkal and Alessia Paccagnini and Anastasios Panagiotelis and Ioannis Panapakidis and Pav{\'i}a, {Jose M.} and Manuela Pedio and Pedregal, {Diego J.} and Pierre Pinson and Patr{\'i}cia Ramos and Rapach, {David E.} and Reade, {J. James} and Bahman Rostami-Tabar and Micha{\l} Rubaszek and Georgios Sermpinis and Shang, {Han Lin} and Evangelos Spiliotis and Syntetos, {Aris A.} and Talagala, {Priyanga Dilini} and Talagala, {Thiyanga S.} and Len Tashman and Dimitrios Thomakos and Thordis Thorarinsdottir and Ezio Todini and {Trapero Arenas}, {Juan Ram{\'o}n} and Xiaoqian Wang and Winkler, {Robert L.} and Alisa Yusupova and Florian Ziel",

note = "Acknowledgments: 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{\textquoteright}s software functionalities. Jakub Bijak{\textquoteright}s work received funding from the European Union{\textquoteright}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{\c c}{\~a}o para a Ci{\^e}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 Award DE200101070. 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{\'i}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{\'i}a d{\textquoteright}Innovaci{\'o}, Universitats, Ci{\`e}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{\'o}n Pol{\'i}tica Cient{\'i}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{\l} 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”.",

year = "2022",

month = jul,

doi = "10.1016/j.ijforecast.2021.11.001",

language = "English",

volume = "38",

pages = "705--871",

journal = "International Journal of Forecasting",

issn = "0169-2070",

publisher = "Elsevier",

number = "3",

}

Petropoulos, F, Apiletti, D, Assimakopoulos, V, Babai, MZ, Barrow, DK, Ben taieb, S, Bergmeir, C, Bessa, RJ, Bijak, J, Boylan, JE, Browell, J, Carnevale, C, Castle, JL, Cirillo, P, Clements, MP, Cordeiro, C, Cyrino Oliveira, FL, De Baets, S, Dokumentov, A, Ellison, J, Fiszeder, P, Franses, PH, Frazier, DT, Gilliland, M, Gönül, MS, Goodwin, P, Grossi, L, Grushka-Cockayne, Y, Guidolin, M, Guidolin, M, Gunter, U, Guo, X, Guseo, R, Harvey, N, Hendry, DF, Hollyman, R, Januschowski, T, Jeon, J, Jose, VRR, Kang, Y, Koehler, AB, Kolassa, S, Kourentzes, N, Leva, S, Li, F, Litsiou, K, Makridakis, S, Martin, GM, Martinez, AB, Meeran, S, Modis, T, Nikolopoulos, K, Önkal, D, Paccagnini, A, Panagiotelis, A, Panapakidis, I, Pavía, JM, Pedio, M, Pedregal, DJ, Pinson, P, Ramos, P, Rapach, DE, Reade, JJ, Rostami-Tabar, B, Rubaszek, M, Sermpinis, G, Shang, HL, Spiliotis, E, Syntetos, AA, Talagala, PD, Talagala, TS, Tashman, L, Thomakos, D, Thorarinsdottir, T, Todini, E, Trapero Arenas, JR, Wang, X, Winkler, RL, Yusupova, A & Ziel, F 2022, 'Forecasting: theory and practice', International Journal of Forecasting, vol. 38, no. 3, pp. 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001

TY - JOUR

T1 - Forecasting

T2 - theory and practice

AU - Petropoulos, Fotios

AU - Apiletti, Daniele

AU - Assimakopoulos, Vassilios

AU - Babai, Mohamed Zied

AU - Barrow, Devon K.

AU - Ben taieb, Souhaib

AU - Bergmeir, Christoph

AU - Bessa, Ricardo J.

AU - Bijak, Jakub

AU - Boylan, John E.

AU - Browell, Jethro

AU - Carnevale, Claudio

AU - Castle, Jennifer L.

AU - Cirillo, Pasquale

AU - Clements, Michael P.

AU - Cordeiro, Clara

AU - Cyrino Oliveira, Fernando Luiz

AU - De Baets, Shari

AU - Dokumentov, Alexander

AU - Ellison, Joanne

AU - Fiszeder, Piotr

AU - Franses, Philip Hans

AU - Frazier, David T.

AU - Gilliland, Michael

AU - Gönül, M. Sinan

AU - Goodwin, Paul

AU - Grossi, Luigi

AU - Grushka-Cockayne, Yael

AU - Guidolin, Mariangela

AU - Guidolin, Massimo

AU - Gunter, Ulrich

AU - Guo, Xiaojia

AU - Guseo, Renato

AU - Harvey, Nigel

AU - Hendry, David F.

AU - Hollyman, Ross

AU - Januschowski, Tim

AU - Jeon, Jooyoung

AU - Jose, Victor Richmond R.

AU - Kang, Yanfei

AU - Koehler, Anne B.

AU - Kolassa, Stephan

AU - Kourentzes, Nikolaos

AU - Leva, Sonia

AU - Li, Feng

AU - Litsiou, Konstantia

AU - Makridakis, Spyros

AU - Martin, Gael M.

AU - Martinez, Andrew B.

AU - Meeran, Sheik

AU - Modis, Theodore

AU - Nikolopoulos, Konstantinos

AU - Önkal, Dilek

AU - Paccagnini, Alessia

AU - Panagiotelis, Anastasios

AU - Panapakidis, Ioannis

AU - Pavía, Jose M.

AU - Pedio, Manuela

AU - Pedregal, Diego J.

AU - Pinson, Pierre

AU - Ramos, Patrícia

AU - Rapach, David E.

AU - Reade, J. James

AU - Rostami-Tabar, Bahman

AU - Rubaszek, Michał

AU - Sermpinis, Georgios

AU - Shang, Han Lin

AU - Spiliotis, Evangelos

AU - Syntetos, Aris A.

AU - Talagala, Priyanga Dilini

AU - Talagala, Thiyanga S.

AU - Tashman, Len

AU - Thomakos, Dimitrios

AU - Thorarinsdottir, Thordis

AU - Todini, Ezio

AU - Trapero Arenas, Juan Ramón

AU - Wang, Xiaoqian

AU - Winkler, Robert L.

AU - Yusupova, Alisa

AU - Ziel, Florian

N1 - Acknowledgments: 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 – Fundação para a Ciência 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 Award DE200101070. 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. 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. 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]. 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. 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”.

PY - 2022/7

Y1 - 2022/7

N2 - 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.

AB - 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.

KW - Review

KW - Encyclopedia

KW - Methods

KW - Applications

KW - Principles

KW - Time series

KW - Prediction

U2 - 10.1016/j.ijforecast.2021.11.001

DO - 10.1016/j.ijforecast.2021.11.001

M3 - Review article

SN - 0169-2070

VL - 38

SP - 705

EP - 871

JO - International Journal of Forecasting

JF - International Journal of Forecasting

IS - 3

ER -

Forecasting: theory and practice

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Bibliographical note

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