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name: forecasting-models description: "Choose and use GluonTS predictors, estimators, forecasts, local baselines, PyTorch estimators, and predictor persistence." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

forecasting-models

Use this sub-skill when the task is to select or operate GluonTS forecasting models after data has already been shaped as a GluonTS dataset.

What this sub-skill covers

• Choosing between local predictors and trainable global estimators.
• Using Estimator.train(...) -> Predictor and Predictor.predict(...) -> Forecast.
• Reading SampleForecast and QuantileForecast outputs: samples, mean, median, quantiles, indexes, and multivariate dimensions.
• Running deterministic local baselines such as seasonal naive, identity, constant, moving-average, mean, and NPTS predictors.
• Constructing and optionally training selected PyTorch estimators with bounded trainer_kwargs on CPU or optional CUDA.
• Serializing and deserializing predictors with Predictor.serialize(...) and Predictor.deserialize(...).

Use the bundled references

• references/model-overview.md — model-selection guidance, local/global split, PyTorch catalog, and legacy backend caveats.
• references/api-reference.md — distilled signatures and key behaviors for predictors, forecasts, local predictors, and PyTorch estimators.
• references/workflows.md — copyable operating recipes for train/predict, baselines, forecast extraction, persistence, PyTorch trainer kwargs, and warm-starting.
• references/troubleshooting.md — failures around optional extras, short histories, features, quantiles, serialization, PyTorch Lightning, CUDA, and legacy MXNet.

Bundled smoke scripts

Run these from any working directory after gluonts is installed:

python path/to/scripts/predictor_persistence_smoke.py --help
python path/to/scripts/predictor_persistence_smoke.py

python path/to/scripts/torch_forecast_smoke.py --help
python path/to/scripts/torch_forecast_smoke.py
python path/to/scripts/torch_forecast_smoke.py --train

predictor_persistence_smoke.py checks a checkout-independent local predictor serialize/reload workflow. torch_forecast_smoke.py constructs a tiny PandasDataset and a bounded PyTorch estimator; by default it does not train, and --train runs a one-epoch tiny smoke when the torch/Lightning extra is available.

Routing hints

• If the task is about building PandasDataset, splitting train/test windows, JSON Lines, or entry schema, use the data-pipelines sub-skill first.
• If the task is about transformation chains, lags, time features, samplers, or instance splitters, use transforms-features first.
• If the task is about accuracy metrics, Evaluator, backtest_metrics, or make_evaluation_predictions, use evaluation-backtesting after producing forecasts.
• If the task is about SageMaker shell, CLI, or optional extension adapters, use deployment-extensions.

Required safety posture

Use CPU-bounded examples unless the user explicitly asks for GPU. Treat CUDA as optional acceleration. Treat MXNet models as legacy/unverified in this skill scope; do not promise that MXNet workflows work unless the user provides and verifies a compatible MXNet environment.