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name: neural-prophet description: "Use NeuralProphet for interpretable time-series forecasting with fit/predict, components, uncertainty, plotting, save/load, and Prophet migration workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

NeuralProphet repo skill

Use this skill when a task involves the Python package neuralprophet / NeuralProphet: Prophet-like time-series forecasting, ds/y dataframes, forecast horizons, trend/seasonality/autoregression/regressor/event components, global multi-series models, uncertainty intervals, plotting, serialization, or TorchProphet migration.

First checks

Install from PyPI for normal use:

python -m pip install neuralprophet
python -c "from neuralprophet import NeuralProphet; print(NeuralProphet)"

For this version, prefer Python supported by the package metadata (>=3.9,<3.13). If imports or fitting fail, read references/troubleshooting.md; known practical compatibility fixes include pandas<3 and setuptools<81 for the verified source snapshot.

Run the bundled diagnostic when the user's environment is uncertain:

python scripts/check_neuralprophet_install.py

Use --check-cuda only when the user explicitly needs CUDA verification. Core workflows are CPU-capable.

Route map

User intent	Load
Quickstart forecasting, dataframe validation, fit, predict, future periods, yhat* columns	sub-skills/core-forecasting/SKILL.md
Trend, changepoints, seasonality, autoregression, lagged/future regressors, events, holidays, global/local multi-series modeling	sub-skills/components-and-exogenous/SKILL.md
Train/validation/test splits, cross-validation, metrics, quantile regression, conformal prediction, uncertainty evaluation	sub-skills/evaluation-and-uncertainty/SKILL.md
CLI/version, logging, seeding, plotting backends, optional extras, save/load, accelerators, TorchProphet/Prophet migration	sub-skills/operations-and-migration/SKILL.md

Shared references

• package-overview.md gives the package purpose, top-level imports, data model, install notes, and workflow map.
• troubleshooting.md covers cross-cutting import, dependency, data, optional-extra, and backend failures.
• repo-provenance.md records the source snapshot used to build this skill; read it before deciding whether to refresh the skill for a changed checkout.
• repo-routing-metadata.json is structured router metadata for managed import tooling.

Common package pattern

import pandas as pd
from neuralprophet import NeuralProphet, set_log_level

set_log_level("ERROR")
df = pd.DataFrame({"ds": timestamps, "y": values})
model = NeuralProphet(epochs=10, accelerator="cpu")
metrics = model.fit(df, freq="D")
future = model.make_future_dataframe(df, periods=30, n_historic_predictions=True)
forecast = model.predict(future)

Add components before fit, validate all required columns before prediction, and keep explicit frequency strings for irregular or calendar-sensitive data.

Do not use this skill when

• The task is only generic PyTorch Lightning training internals with no NeuralProphet forecasting API.
• The task is general repository maintenance or contribution work rather than using the package.
• The user needs another forecasting library with incompatible data schema or model APIs.
• The task requires benchmark-scale training, external tutorial downloads, credentials, services, or hardware-specific claims that have not been separately authorized and verified.