---

name: data-preparation description: "Validate and explain TimeMixer dataset layouts, file names, splits, date and frequency features, and benchmark-specific data conventions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TimeMixer Data Preparation

Use this sub-skill for dataset layout questions, file checks, and small validation tasks.

Use when

• a custom CSV must be checked for date, target, and numeric feature columns
• an ETT, M4, PEMS, Solar, anomaly, or UEA dataset needs layout confirmation
• split sizes, windowability, or time-feature conventions need to be explained
• a future agent needs a safe validator instead of a training or download run

Do not use when

• the task is about command construction for forecasting experiments
• the task is about model tensor shapes, embeddings, or architecture internals
• the task requires dataset downloads, benchmark training, or native example execution

What this sub-skill covers

• dataset-key to loader mapping
• required file names and column conventions
• split behavior and window-length implications
• date parsing, freq handling, and generated time features
• benchmark-specific quirks for custom CSV, M4, PEMS, Solar, anomaly CSV/NPY, and UEA .ts files

Bundled assets

• references/data-formats.md
• references/troubleshooting.md
• scripts/validate_timemixer_data.py

Quick workflow

1. Read references/data-formats.md for the expected layout.
2. Run python scripts/validate_timemixer_data.py --help to inspect supported checks.
3. Validate the candidate dataset tree with the bundled script.
4. If the issue is actually about forecast commands or model internals, hand off to the matching sub-skill.

Routing

• Forecast command assembly and benchmark recipe questions: forecasting-experiments
• Model classes, tensor shapes, and architecture behavior: model-architecture
• Imputation, anomaly logic, or classification workflow details: universal-tasks