Manuel05/04/2023, 10:12 AM
Kin Gtz. Olivares05/04/2023, 2:31 PM
, as a default for which we did not include the possibility to include
, as a tunable hyperparameter. At this point we have the ability to determine the
independently but have not taken out the protections in the
class, here: https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_base_auto.py#L67 In the meantime what I have observed with the number of components is that it follows a classic bias-variance u shape behavior like the ones in this plot.
parameter to distinguish the name columns between the results of different GMMs in the final