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From what I understood: • You have stark scale differences between bottom and top levels. • Due to the scale difference, absolute metrics like RMSE/MAE prioritize the top levels. • You used SMAPE to help with the top-bottom scale difference. • You wonder if there are weighted losses in NF Answering the questions: • Like SMAPE, probabilistic losses (DistributionLoss, PMM, GMM...) are scale independent by definition. • Regarding weighted losses, check scale-independent errors and weighted MQLoss here is the NeuralForecast losses catalog. • Before ditching the probabilistic approach I recommend you to carefully explore: ◦ Use HierarchicalForecast tutorial to set an accuracy expectation. ◦ Explore your forecasting features, control for seasonalities if any. ◦ Model hyperparameters (learning rate and random seed in particular). Hope this helps.

@Kin Gtz. Olivares Thanks! I have yearly seasonality, what do you mean by "control for seasonalities"?

You can add month dummies, like the ones in this example: https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html