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Hi @Eitan Turok, In the NeuralForecast library we are prioritizing modularity. During instantiation our models allow you to customize the loss functions that you can choose from a catalog, or in its absence you can use your own. In particular for the under and overshooting loss (inbalanced losses), we have

QuantileLos

and

MQLoss

that are based on the NewsVendor problem. Here is the NeuralForecast losses catalog: https://nixtla.github.io/neuralforecast/losses.pytorch.html Here is an MQLoss example: https://nixtla.github.io/neuralforecast/examples/uncertaintyintervals.html