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The value of feedback in forecasting competitions

Author

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  • George Athanasopoulos
  • Rob J Hyndman

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

  • George Athanasopoulos & Rob J Hyndman, 2011. "The value of feedback in forecasting competitions," Monash Econometrics and Business Statistics Working Papers 3/11, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2011-3
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp3-11.pdf
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    References listed on IDEAS

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    1. Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    4. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    5. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    Cited by:

    1. Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849.
    2. 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.
    3. 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.
    4. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    5. 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.
    6. 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.
    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. 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.
    9. 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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    More about this item

    Keywords

    Forecasting competition; feedback.;

    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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