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name: forecasting-workflows description: "Use Darts core forecasting workflows, model selection, covariates, probabilistic prediction, and fit/predict validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Forecasting workflows

Use this sub-skill for Darts non-neural/core forecasting: model selection, fit()/predict(), covariate-capable regression/global models, probabilistic forecasts, historical forecasts/backtesting overview, and forecast troubleshooting.

Read first

• references/model-selection.md for model-family choices, optional dependency boundaries, and covariate support.
• references/workflows.md for baseline/core forecast, probabilistic forecast, lagged regression covariates, and validation patterns.
• references/api-reference.md for fit/predict conventions and common model constructors.
• references/troubleshooting.md for unsupported covariates, wrong horizon, probabilistic output, and optional dependency errors.
• scripts/forecasting_smoke.py for a tiny baseline + ExponentialSmoothing smoke.

Route by task

• First forecast or baseline: start with NaiveSeasonal, NaiveDrift, or ExponentialSmoothing before complex models.
• No torch allowed: stay in this sub-skill and use core/statistical/regression models; do not route to neural models.
• Covariates with lagged/global models: use LinearRegressionModel or other regression/global models with lags, lags_past_covariates, and/or lags_future_covariates.
• Model rejects covariate arguments: do not force them. Switch to a model family that supports the requested covariate type.
• Probabilistic forecast: verify the model supports samples/likelihood/quantiles, call predict(..., num_samples=...) when supported, then route metric details to ../evaluation-and-explainability/.
• Neural model, darts[torch], GPU, checkpoint, or foundation wrapper: route to ../torch-and-foundation-models/.

Safe check

python scripts/forecasting_smoke.py --compact

The smoke uses generated monthly data, validates forecast length and stochastic samples, and does not read repo examples or notebooks.

Boundaries

This sub-skill does not own Darts data construction, preprocessing/covariate generation, PyTorch trainer configuration, anomaly detection, or final metric interpretation. Cross-link instead of duplicating those workflows.