Forecast Selection and Representativeness
Published Online:18 Jul 2022https://doi.org/10.1287/mnsc.2022.4485
Supplemental Material
The replication files for this article are available HERE.
- Cited by
- International Journal of Forecasting, Vol. 42, No. 2
- 20 March 2026 | Big Data and Cognitive Computing, Vol. 10, No. 3
- 8 October 2025 | International Journal of Production Research, Vol. 64, No. 1
- 25 July 2025 | Management Science, Vol. 72, No. 1
- Annals of Tourism Research, Vol. 116
- European Journal of Operational Research, Vol. 328, No. 2
- IEEE Transactions on Engineering Management, Vol. 73
- 29 May 2025 | INFORMS Journal on Applied Analytics, Vol. 55, No. 6
- 11 December 2024 | International Journal of Production Research, Vol. 63, No. 9
- 1 January 2025 | SSRN Electronic Journal, Vol. 23
- 27 December 2023 | Journal of the Operational Research Society, Vol. 75, No. 3
- 1 January 2024 | SSRN Electronic Journal, Vol. 23
- Decision Support Systems, Vol. 174
- 15 May 2023 | Journal of Operations Management, Vol. 69, No. 6
- 20 June 2023 | Forecasting, Vol. 5, No. 2
Volume 69, Issue 5
May 2023
Pages 2547-3155, iii-iv
Article Information
Supplemental Material
Metrics
Information
- Received:September 11, 2019
- Accepted:January 18, 2022
- Published Online:July 18, 2022
Copyright © 2022, INFORMS
Cite as
Fotios Petropoulos, Enno Siemsen (2022) Forecast Selection and Representativeness. Management Science 69(5):2672-2690.
https://doi.org/10.1287/mnsc.2022.4485
Keywords
The authors thank Konstantinos Nikolopoulos, Paul Goodwin, Evangelos Spiliotis, and Len Tashman for their feedback and comments. This project made use of the Balena High Performance Computing (HPC) Service at the University of Bath. The authors used R statistical software (3.4.3) and the forecast (8.9) and smooth (2.5.3) packages. The MCB analysis made use of the nemenyi() function of the tsutils (0.9.2) package. The data are available under the packages Mcomp (2.8) and M4comp2018 (0.2.0).
