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name: "probabilistic-losses" description: "Routes NeuralForecast quantile, distribution, robust-loss, and prediction-interval workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

probabilistic-losses

Use this sub-skill when the user is choosing a point, quantile, distribution, or robust loss, debugging quantile/level conversions, or matching a training loss to prediction intervals or simulation outputs.

What this route covers

• Point losses such as MAE, MSE, RMSE, MAPE, SMAPE, MASE, and relMSE.
• Quantile and probabilistic losses such as MQLoss, IQLoss, DistributionLoss, PMM, GMM, NBMM, and sCRPS.
• Robust losses such as HuberLoss, TukeyLoss, HuberQLoss, HuberMQLoss, HuberIQLoss, and FreDF.
• Level/quantile translation and prediction-interval alignment.

What this route does not cover

• The panel dataframe contract.
• Choosing the model family.
• Auto*/distributed execution.
• Core fit/predict orchestration beyond what is needed to explain the loss.

If the user only needs to run a model after picking the loss, route to ../core-forecasting/SKILL.md. If the data layout is causing the failure, route to ../data-and-exogenous/SKILL.md.

Read these bundled references

• ../../references/losses-reference.md for the full loss-family overview.
• ../../references/api-reference.md for the verified loss signatures.
• references/troubleshooting.md for quantile, compatibility, and mask errors.

Run this bundled script

• ../../scripts/check_losses.py for a small deterministic loss check.

Common triggers

• "Which loss should I use for quantiles?"
• "How do I get prediction intervals?"
• "Why do duplicate quantiles warn?"
• "Why does valid_loss have to match the training loss?"
• "What does horizon_weight do?"

Minimal route

1. Read ../../references/losses-reference.md.
2. Use ../../scripts/check_losses.py if you want a quick smoke of the loss surface.
3. Read references/troubleshooting.md when the loss configuration fails.

Decision cues

• If the user already knows the loss and needs a model, route to model-selection.
• If the user needs the panel schema or future dataframe shape, route to data-and-exogenous.
• If the user needs the actual training run, route to core-forecasting.

Safe default

When in doubt, start with MAE for a point forecast or MQLoss(level=[80, 90]) when the user explicitly wants quantiles.