---

name: "neuralforecast" description: "Routes NeuralForecast time-series forecasting, data, model-selection, loss, tuning, and deployment workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

NeuralForecast

Use this skill when the task is about the neuralforecast package and its panel time-series forecasting workflows: fitting models, predicting horizons, choosing model families, validating panel data, selecting losses, tuning with Ray or Optuna, working with distributed Spark data, or saving and reloading a fitted forecast bundle.

Install

For a public install:

python -m pip install neuralforecast

For a local checkout:

python -m pip install -e .

The package requires Python 3.10+ and is routinely inspected here with Python 3.11. Install optional extras only when the route needs them, such as spark for distributed workflows or onnx / mlflow / transformers / xlstm for specialized extension recipes.

Minimal import check

python -I -c "from neuralforecast import NeuralForecast; from neuralforecast.models import NHITS; from neuralforecast.utils import generate_series; print('ok')"

If that fails, read references/troubleshooting.md before doing anything else.

Route map

User task family	Read this sub-skill	Typical helper
Fit, predict, backtest, save/load, simulate, explain	sub-skills/core-forecasting/SKILL.md	scripts/core_smoke.py
Panel dataframe layout, exogenous variables, categorical features, scalers	sub-skills/data-and-exogenous/SKILL.md	scripts/validate_panel.py
Choose a model family or compare constructors	sub-skills/model-selection/SKILL.md	scripts/list_models.py
Quantile, distribution, robust, and interval losses	sub-skills/probabilistic-losses/SKILL.md	scripts/check_losses.py
Auto* search, Ray/Optuna, or Spark distributed paths	sub-skills/tuning-and-distributed/SKILL.md	scripts/check_auto_config.py
Save/load, ONNX, MLflow, docs, and extension work	sub-skills/deployment-and-extension/SKILL.md	scripts/check_serialization.py

Shared references

• references/repo-provenance.md before deciding whether the skill matches the current checkout or should be refreshed.
• references/repo-routing-metadata.json for managed repo-skill routing.
• references/api-reference.md for verified signatures.
• references/data-formats.md for the panel schema and exogenous-column rules.
• references/model-overview.md for the model catalog and capability flags.
• references/losses-reference.md for loss and prediction-interval rules.
• references/workflows.md for compact end-to-end recipes.
• references/tuning-distributed.md for Auto*, Ray, Optuna, and Spark details.
• references/deployment-extension.md for serialization and extension guidance.
• references/troubleshooting.md for cross-cutting failures and recovery.

Shared scripts

• scripts/core_smoke.py — tiny fit/predict smoke, good first check for core package health.
• scripts/validate_panel.py — dataframe schema and panel-layout validator.
• scripts/list_models.py — print the exported model catalog and capability flags.
• scripts/check_losses.py — deterministic loss sanity checks.
• scripts/check_auto_config.py — Auto* and backend option sanity check.
• scripts/check_serialization.py — save/load round-trip smoke.

How to route

1. Decide whether the request is about the panel schema, model choice, core forecasting, loss selection, tuning/distributed execution, or deployment.
2. Read the matching sub-skill first.
3. Use the shared references for API signatures, workflows, and troubleshooting.
4. Use the shared scripts when a safe tiny smoke or validator will answer the question faster than reading prose.

Fast defaults

• If the user is unsure which model to use, open model-selection first.
• If the user shows a dataframe or schema error, open data-and-exogenous.
• If the user asks for "the usual quickstart," open core-forecasting and run scripts/core_smoke.py.
• If the user asks about quantiles or intervals, open probabilistic-losses.
• If the user asks about Ray, Optuna, Spark, or Auto*, open tuning-and-distributed.
• If the user asks about save/load or extending the package, open deployment-and-extension.

Staleness check

Read references/repo-provenance.md before trusting version-sensitive details. If the current checkout commit or public API has moved on, refresh this skill.