That was true in the first Makridakis competition ("M1") in 1982, and possibly until M4 in 2018, but both M5 and M6 were won by what would generally be considered relatively sophisticated methods (e.g. LightGBM).

The Wikipedia article doesn't have that much detail on M5 or M6, but the M5 papers are in the International Journal of Forecasting[1] and M6 should be published later this year (there's already a preprint on arxiv [2]).

I recently spent some time looking into the history and results of the M competitions and had a chance to speak to Professor Makridakis about them, as well as the winners of each of the M6 competition tracks [3]. While the methods have become more sophisticated, some conclusions from M1 still seem to hold: in particular, that there is no overall "best" method, and that the winning method tends to be different for different types of data, time horizons, and evaluation metrics.

[1]: https://www.sciencedirect.com/science/article/pii/S016920702... [2]: https://arxiv.org/abs/2310.13357 [3]: https://mlcontests.com/state-of-competitive-machine-learning...

Our basic low-dimensional parametric model landed No1 at the SKU level at the M5, see my lecture https://www.lokad.com/tv/2022/1/5/no1-at-the-sku-level-in-th... (more references at the bottom)

Interesting, thanks for sharing!

A recent thread on Amazon’s new Chronos forecasting model showed that an ensemble of simple models outperformed it (a highly parametrized transformed model) on the M competition datasets.

https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...