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#neural-forecast
Title
# neural-forecast
m

Manuel

05/04/2023, 10:12 AM
With AutoNHITS, is there any way to include the loss (with a different val_loss) in the search space as well? For example, I would like to search for the best loss among GMM, PMM and NBMM (also optimizing their "n_components" parameter, e.g. between 1 and 10) so as to minimize the sCRPS validation loss. Thanks.
k

Kin Gtz. Olivares

05/04/2023, 2:31 PM
Hey @Manuel, Thanks for using NeuralForecast. Can you open a github issue to have this in my TODO list? In the past we had
valid_loss=loss
, as a default for which we did not include the possibility to include
loss
, as a tunable hyperparameter. At this point we have the ability to determine the
valid_loss
independently but have not taken out the protections in the
AutoBase
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.
Another recomendation @Manuel, is to momentarily use the
alias
parameter to distinguish the name columns between the results of different GMMs in the final
Y_hat_df
dataframe.
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