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name: data-pipelines description: "Prepare GluonTS datasets from pandas, list, JSON Lines, and optional Arrow inputs; split entries into train/test windows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

GluonTS data-pipelines sub-skill

Use this sub-skill when a task needs GluonTS Dataset objects, DataEntry validation, or train/test windows before modeling, transformation, or evaluation.

Route by task

• Pandas or in-memory tables: read references/data-formats.md and references/workflows.md.
• Long dataframes with item/static/dynamic columns: read references/data-formats.md and references/workflows.md.
• List, JSON Lines, gzipped JSON Lines, or optional Arrow/Parquet files: read references/data-formats.md and references/workflows.md.
• Train/test slicing or rolling evaluation windows: read references/workflows.md and use scripts/dataset_split_smoke.py as the local sanity check.
• Validation failures or shape/frequency errors: read references/troubleshooting.md.

Safe smoke check

After installing gluonts with its base dependencies, run:

python sub-skills/data-pipelines/scripts/dataset_split_smoke.py

The script creates a tiny pandas-backed dataset, splits off the trailing prediction horizon, validates generated input/label entries, and prints a concise success summary. It performs no network, plotting, downloads, training, or checkout-relative reads.

Boundaries

This sub-skill covers dataset construction and splitting only. Use sibling sub-skills for transforms/time features, estimator or predictor training, forecast evaluation, and deployment/extension adapters. MXNet-specific workflows are legacy/optional and are not verified by this data-pipeline guidance.