  Competitions play an invaluable role in the field of forecasting, as
exemplified through the recent M4 competition. The competition received
attention from both academics and practitioners and sparked discussions around
the representativeness of the data for business forecasting. Several
competitions featuring real-life business forecasting tasks on the Kaggle
platform has, however, been largely ignored by the academic community. We
believe the learnings from these competitions have much to offer to the
forecasting community and provide a review of the results from six Kaggle
competitions. We find that most of the Kaggle datasets are characterized by
higher intermittence and entropy than the M-competitions and that global
ensemble models tend to outperform local single models. Furthermore, we find
the strong performance of gradient boosted decision trees, increasing success
of neural networks for forecasting, and a variety of techniques for adapting
machine learning models to the forecasting task.
