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name: core-forecasting description: "Fit NeuralProphet models, validate time-series dataframes, build future dataframes, and run basic forecasts." disable-model-invocation: true metadata: disco-role: operating license: MIT

Core forecasting

Use this sub-skill when the task is to get a NeuralProphet forecast running: prepare a dataframe, choose core model settings, call fit, build a future dataframe, call predict, or understand the yhat* forecast columns.

Load this when the user asks to

• Create a first NeuralProphet model from a pandas dataframe with ds and y columns.
• Validate or repair timestamp, target, frequency, missing-value, duplicate-date, or optional ID column issues before fitting.
• Choose basic constructor settings such as n_lags, n_forecasts, epochs, batch_size, learning_rate, collect_metrics, and accelerator='cpu'.
• Forecast future periods with make_future_dataframe and explain historic versus future rows.
• Interpret the shape of forecast dataframes, especially yhat1, yhat2, ... columns.

Start here

1. Read workflows.md for the minimal fit/predict recipe, dataframe contract, future dataframe patterns, and validation checklist.
2. Read api-reference.md when you need verified signatures and parameter defaults for NeuralProphet, fit, predict, make_future_dataframe, and dataframe utility helpers.
3. Read troubleshooting.md when a fit/predict run fails, frequency inference is wrong, metrics is None, or dependency versions break current NeuralProphet code.
4. Run scripts/smoke_forecast.py to prove a tiny CPU fit and prediction can run without network access.
5. Run scripts/validate_neuralprophet_dataframe.py before fitting user data when timestamps, y, ID, or frequency quality is uncertain.

Boundaries

Stay here for core model execution and dataframe readiness. Route elsewhere for:

• Trend, seasonality, autoregression, regressors, events, holidays, or global/local component design: ../components-and-exogenous/SKILL.md.
• Validation splits, cross-validation, metrics, quantiles, conformal prediction, and uncertainty evaluation: ../evaluation-and-uncertainty/SKILL.md.
• CLI/version checks, plotting backends, save/load, logging, seeding, accelerators, and TorchProphet migration: ../operations-and-migration/SKILL.md.

Fast decision checklist

• Dataframe has parseable ds; training data has y; multi-series data uses ID.
• Use an explicit pandas frequency string such as "D", "H", "MS", or "5min" when freq='auto' would be ambiguous.
• Start with CPU, small epochs, and disabled seasonalities for smoke tests; add components only after core fit/predict works.
• If n_lags > 0, preserve enough history and use n_historic_predictions deliberately when constructing future data.
• Do not rely on original repository notebooks or tests at runtime; use the bundled references and scripts here.