---

name: gluonts description: "Use GluonTS for probabilistic time-series forecasting data pipelines, transformations, PyTorch/local models, evaluation, backtesting, shell deployment, and optional adapters." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

GluonTS repo skill

Use this skill when a task involves GluonTS time-series forecasting: PandasDataset, ListDataset, DataEntry fields, train/test windows, transformations, Estimator/Predictor, PyTorch forecasting models, forecast evaluation metrics, gluonts.shell, or optional forecasting adapters.

Start here

1. Check the package snapshot and selected scope in references/repo-provenance.md when freshness matters.
2. Read references/package-overview.md for install commands, extras, backend boundaries, and route selection.
3. Run the environment diagnostic when imports or extras are uncertain:

python scripts/check_gluonts_env.py --check-torch --check-shell

For public package installs, use the smallest extra set required by the task:

pip install gluonts
pip install "gluonts[torch]"   # PyTorch estimator workflows
pip install "gluonts[shell]"   # gluonts.shell train/serve workflows

Route by task

Task signal	Read
DataFrames, long dataframes, item/static/dynamic columns, list/file datasets, JSON Lines, optional Arrow, or train/test windows	sub-skills/data-pipelines/SKILL.md
Transformation chains, time features, lag lists, missing-value indicators, samplers, or InstanceSplitter configuration	sub-skills/transforms-features/SKILL.md
Estimator/Predictor concepts, local baselines, PyTorch estimators, forecast objects, persistence, CPU/GPU trainer kwargs	sub-skills/forecasting-models/SKILL.md
make_evaluation_predictions, Evaluator, item metrics, aggregate metrics, backtesting, or gluonts.ev metric questions	sub-skills/evaluation-backtesting/SKILL.md
python -m gluonts, python -m gluonts.shell, SageMaker-style train/serve payloads, batch transform JSON Lines, or optional extension adapters	sub-skills/deployment-extensions/SKILL.md
Cross-cutting install/import/data/backend problems	references/troubleshooting.md

Common workflow skeleton

Use this sequence for end-to-end forecasting tasks:

1. Build a dataset with PandasDataset or ListDataset using the data-pipelines sub-skill.
2. Split the trailing horizon with split(dataset, offset=-prediction_length) or a date-based split.
3. Add transformations/time features only when a model workflow requires manual preprocessing; many estimators construct their own transformations internally.
4. Choose a local predictor for a quick baseline or a PyTorch estimator for learned global forecasting.
5. Generate forecasts with predictor.predict(...) or make_evaluation_predictions(...).
6. Evaluate with Evaluator or backtest_metrics and inspect item metrics before trusting aggregates.
7. Persist or deploy only after the predictor and data schema are verified on a tiny local case.

Bundled checks

Helper	Use
scripts/check_gluonts_env.py	Imports GluonTS core and selected optional extras; can report optional CUDA availability.
sub-skills/data-pipelines/scripts/dataset_split_smoke.py	Validates PandasDataset + split behavior on deterministic local data.
sub-skills/transforms-features/scripts/transform_feature_smoke.py	Validates missing-value indicators, time features, lags, and instance splitting.
sub-skills/forecasting-models/scripts/predictor_persistence_smoke.py	Serializes/reloads a local predictor and compares deterministic forecasts.
sub-skills/forecasting-models/scripts/torch_forecast_smoke.py	Checks PyTorch estimator construction and, when requested, a tiny bounded train/predict path.
sub-skills/evaluation-backtesting/scripts/evaluate_synthetic_forecast.py	Runs a deterministic forecast evaluation/backtest smoke and optional item-metrics CSV output.
sub-skills/deployment-extensions/scripts/shell_payload_validator.py	Validates SageMaker-style JSON inference payloads before shell/serve use.

Boundaries and cautions

• CPU is sufficient for the verified required workflows. CUDA is optional acceleration for PyTorch training and must be probed before claiming GPU execution.
• MXNet workflows are legacy/unverified in this generated skill. If the user explicitly asks for MXNet, prepare a separate compatible environment and verify it before use.
• Optional adapters such as Prophet, R forecast, statsforecast, hierarchicalforecast, and rotbaum require their documented extras and sometimes external runtimes.
• Keep smoke tests deterministic and small. Avoid network datasets, benchmark-scale notebooks, Docker builds, AWS calls, long training, or credentialed services unless the user explicitly asks and the environment is prepared.
• Runtime guidance in this skill is self-contained. Do not require the original GluonTS repository files for ordinary package use.