---

name: "model-selection" description: "Routes NeuralForecast model-family choice, constructor comparison, and optional-dependency caveat workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

model-selection

Use this sub-skill when the user asks which NeuralForecast model family to use, how two constructors differ, whether a model supports exogenous variables or multivariate output, or whether an optional dependency is required.

What this route covers

• Model-family comparison and capability selection.
• Constructor differences and important input constraints.
• Multivariate vs univariate decisions.
• Optional-dependency caveats for TimeLLM and xLSTM.

What this route does not cover

• The panel dataframe contract.
• Detailed fit/predict execution.
• Loss construction.
• Auto*/distributed search mechanics.

If the user already knows the model and just needs to run it, route to ../core-forecasting/SKILL.md. If the user still needs to validate columns or exogenous layout, route to ../data-and-exogenous/SKILL.md.

Read these bundled references

• ../../references/model-overview.md for the public catalog and capability flags.
• ../../references/api-reference.md for verified constructor signatures.
• references/troubleshooting.md for n_series, exogenous, and optional-dep failures.

Run this bundled script

• ../../scripts/list_models.py for a live model catalog and capability dump.

Common triggers

• "Which model should I use for this time series?"
• "What is the difference between NHITS and NBEATSx?"
• "Does this model support exogenous variables?"
• "Why does a multivariate model ask for n_series?"
• "Why can't I import TimeLLM or xLSTM?"

Minimal route

1. Read ../../references/model-overview.md to narrow the family.
2. Use ../../scripts/list_models.py to confirm what the installed package exports.
3. Read ../../references/api-reference.md when the constructor arguments need to be checked.
4. Read references/troubleshooting.md if the chosen model rejects the task.

Decision cues

• If the user needs quantiles, intervals, or loss choice, route to probabilistic-losses.
• If the user needs backtesting or save/load after the model is chosen, route to core-forecasting.
• If the issue is the dataframe or exogenous columns themselves, route to data-and-exogenous.

Safe default

When in doubt, prefer the simplest model that supports the requested data shape. That usually means a baseline or a decomposition-style model first, then a more expressive family if the user needs it.