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The value of feedback in forecasting competitions

Author

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  • Athanasopoulos, George
  • Hyndman, Rob J.

Abstract

In this paper we challenge the traditional design used for forecasting competitions. We implement an online competition with a public leaderboard that provides instant feedback to competitors who are allowed to revise and resubmit forecasts. The results show that feedback significantly improves forecasting accuracy.

Suggested Citation

  • Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:845-849
    DOI: 10.1016/j.ijforecast.2011.03.002
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    Cited by:

    1. Garcia Martinez, Marian, 2015. "Solver engagement in knowledge sharing in crowdsourcing communities: Exploring the link to creativity," Research Policy, Elsevier, vol. 44(8), pages 1419-1430.
    2. Erhan Bilal & Janusz Dutkowski & Justin Guinney & In Sock Jang & Benjamin A Logsdon & Gaurav Pandey & Benjamin A Sauerwine & Yishai Shimoni & Hans Kristian Moen Vollan & Brigham H Mecham & Oscar M Rue, 2013. "Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-16, May.
    3. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    4. Rianne Legerstee & Philip Hans Franses, 2014. "Do Experts’ SKU Forecasts Improve after Feedback?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 69-79, January.
    5. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
    6. Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849.
    7. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "The M5 competition: Background, organization, and implementation," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1325-1336.
    8. Kaltsounis, Anastasios & Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1404-1412.
    9. Spyros Makridakis & Chris Fry & Fotios Petropoulos & Evangelos Spiliotis, 2022. "The Future of Forecasting Competitions: Design Attributes and Principles," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 96-113, April.
    10. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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