[borroka]

I haven't followed prediction contests for a while because, frankly, the field has moved on (more sideways, actually, with LLMs).

When I used to follow, until a few years ago, the winning models were ensembles of ensembles (e.g., RF is an ensemble). The fact that the best single models are ensembles, or evolutions of ensembles, is therefore not surprising.

When dealing with numerical data, squeezing blood from the stones, which is what happens in the latter stages of the prediction competition, is very rarely worth squeezing in the real world. When the model is not mechanistic but only correlative (almost all models are not purely correlative or mechanistic, anyway), getting to the last decimal place of mean absolute error or a similar metric requires building an increasingly complex structure over which we have little control upon a building that has its foundation of sand. All it takes is a little wind, such as a change in the distribution of data over time-which always happens-and unstable structures are bound to collapse.