Kaggle forecasting competitions: An overlooked learning opportunity

Casper Solheim Bojer and Jens Peder Meldgaard

International Journal of Forecasting, 2021, vol. 37, issue 2, 587-603

Abstract: We review the results of six forecasting competitions based on the online data science platform Kaggle, which have been largely overlooked by the forecasting community. In contrast to the M competitions, the competitions reviewed in this study feature daily and weekly time series with exogenous variables, business hierarchy information, or both. Furthermore, the Kaggle data sets all exhibit higher entropy than the M3 and M4 competitions, and they are intermittent.

Keywords: Time series methods; M competitions; Business forecasting; Forecast accuracy; Machine learning methods; Benchmarking; Time series visualization; Forecasting competition review (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:2:p:587-603

DOI: 10.1016/j.ijforecast.2020.07.007

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