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
arXiv 4 Dec 2020 · Statistics — Applications · publishedInternational Journal of Forecasting (2022) · 843 citations (OpenAlex)
arXiv:2012.03854 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Makridakis, S., Spiliotis, E., Assimakopoulos, V (2020) c self | 0.874 | 9 | 2 | 100% |
| 2 | Del Negro, M., Schorfheide, F (2013) DSGE model-based forecasting | 0.874 | 7 | 2 | 100% |
| 3 | Önkal, D., Gönül, M. S., De Baets, S (2019) Trusting forecasts | 0.874 | 7 | 2 | 100% |
| 4 | Salinas, D., Bohlke-Schneider, M., Callot, L., Medico, R., Gasthaus, J (2019) a | 0.874 | 6 | 2 | 100% |
| 5 | Chou, R. Y., Wu, C. C., Liu, N (2009) Forecasting time-varying covariance with a range-based dynamic conditional correlation model | 0.874 | 5 | 2 | 100% |
| 6 | Fiszeder, P., Fadziński, M., Molnár, P (2019) Range-based DCC models for covariance and value-at-risk forecasting self | 0.874 | 5 | 2 | 100% |
| 7 | Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman,… (2016) Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond self | 0.874 | 5 | 2 | 100% |
| 8 | Athanasopoulos, G., Ahmed, R. A., Hyndman, R. J (2009) Hierarchical forecasts for Australian domestic tourism | 0.811 | 4 | 2 | 100% |
| 9 | Cardani, R., Paccagnini, A., Villa, S (2019) Forecasting with instabilities: An application to DSGE models with financial frictions self | 0.811 | 4 | 2 | 100% |
| 10 | Croston, J. D (1972) Forecasting and stock control for intermittent demands | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 2139 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.