---

name: "core-forecasting" description: "Routes NeuralForecast fit, predict, cross-validation, in-sample prediction, simulation, explanation, and save/load workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

core-forecasting

Use this sub-skill when a user wants to run the main NeuralForecast workflow end to end: build a NeuralForecast object, fit models, predict future values, run cross-validation, inspect in-sample forecasts, simulate paths, explain predictions, or save and reload fitted models.

What this route covers

• NeuralForecast(...) construction.
• fit, predict, cross_validation, and predict_insample.
• simulate and explain for supported models.
• save and load for persistence and portability.
• Validation failures that belong to the core workflow, such as missing validation data for early stopping, short series, and future exogenous mismatches.

What this route does not cover

• Data schema design, categorical encodings, and scaler setup in depth.
• Choosing a model family or understanding model-specific constraints.
• Loss construction details beyond what is needed to use the core workflow.
• Auto* tuning or distributed Spark/Ray execution.

If the request is really about dataframe layout, exogenous columns, or scaling, open ../data-and-exogenous/SKILL.md instead. If the request is about which model family to use, open ../model-selection/SKILL.md.

Read these bundled references

• ../../references/api-reference.md for verified constructor and method signatures.
• ../../references/workflows.md for quickstart, cross-validation, prediction intervals, simulation, explanation, and save/load recipes.
• references/troubleshooting.md for core-workflow failures and recovery steps.

Run these bundled scripts

• ../../scripts/core_smoke.py for a tiny fit/predict smoke check.
• ../../scripts/check_serialization.py for a small save/load round-trip.

Common triggers

• "fit a NeuralForecast model"
• "predict the next horizon"
• "cross-validate this forecast"
• "save and load the fitted model"
• "simulate forecast paths"
• "why does early stopping fail"
• "why is predict_insample empty"

Minimal route

1. Read ../../references/workflows.md for the exact command pattern.
2. Read ../../references/api-reference.md when you need constructor or method arguments.
3. Use ../../scripts/core_smoke.py when you need a safe tiny smoke.
4. Use references/troubleshooting.md when the user shows an error.

Decision cues

• If the user needs unique_id, ds, y, or exogenous column rules, route to data-and-exogenous.
• If the user needs a model choice, alias, or capability comparison, route to model-selection.
• If the user needs quantiles, intervals, or loss choices, route to probabilistic-losses.
• If the user needs Ray, Optuna, or Spark tuning, route to tuning-and-distributed.

Safe default

When in doubt, use ../../scripts/core_smoke.py first. It is the fastest way to confirm that the core package, a model, and a forecast round-trip all work in this environment.