https://en.wikipedia.org/wiki/Makridakis_Competitions
Makridakis and Hibon reached the sad conclusion that "statistically sophisticated and complex methods do not necessarily provide more accurate forecasts than simpler ones."
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...
https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...