---

name: data-and-cli description: "Prepare Time-Series-Library data layouts, choose safe run.py flags, validate local files, and understand checkpoints, result folders, and smoke-test commands." disable-model-invocation: true metadata: disco-role: operating license: MIT

TSLib Data and CLI

Use this sub-skill when the task is about getting a TSLib command to start correctly: local data files, run.py flags, dataset names, CPU/GPU flags, smoke checks, output locations, or confusing path/download behavior.

Route Here

• Choose or validate --task_name, --data, --root_path, --data_path, --features, --target, and --freq.
• Prepare a local custom CSV, ETT-style CSV, M4 folder, anomaly folder, or UEA .ts folder.
• Debug Hugging Face fallback/download surprises when local files are missing.
• Interpret checkpoints/, results/, test_results/, m4_results/, and result text file locations.
• Decide whether to use --no_use_gpu, --gpu, --gpu_type, --use_multi_gpu, or CUDA_VISIBLE_DEVICES for a safe check.
• Build a tiny smoke-test dataset before routing to a task-specific sub-skill.

Reroute

• Long-term, short-term/M4, exogenous, or zero-shot forecasting details: use ../forecasting/SKILL.md.
• Imputation, anomaly detection, or classification recipes: use ../imputation-anomaly-classification/SKILL.md.
• Model catalog, optional Mamba/LTSM dependencies, augmentation flags, or adding a model: use ../foundation-models-and-customization/SKILL.md.
• Cross-cutting installation failures: start with ../../references/installation-and-environment.md and ../../references/troubleshooting.md.

Fast Preflight

From this sub-skill directory, point helpers at the user's TSLib checkout:

python ../../scripts/check_tslib_environment.py --repo-root /path/to/Time-Series-Library --check-torch --check-core-imports
python ../../scripts/create_tiny_tslib_dataset.py --output /path/to/Time-Series-Library/dataset/tiny-custom/tiny.csv
python scripts/validate_tslib_data.py \
  --task long_term_forecast --data custom \
  --root-path /path/to/Time-Series-Library/dataset/tiny-custom \
  --data-path tiny.csv --target OT

Then run a task smoke from the TSLib checkout with --no_use_gpu, --train_epochs 1, --num_workers 0, and small windows before scaling up.

Data Routing Rules

1. Match --task_name to a loader-compatible --data value. For example, classification normally uses --data UEA, while anomaly detection uses one of PSM, MSL, SMAP, SMD, or SWAT.
2. For local CSV forecasting/imputation, make sure --root_path points to the directory and --data_path is the filename. If the file is absent, the loader may try a Hub dataset whose config name is derived from the filename.
3. For custom, include a date column and a numeric target column. --target defaults to OT.
4. For UEA, --model_id is the dataset name used to locate <DatasetName>_TRAIN.ts and <DatasetName>_TEST.ts.
5. For benchmark scripts, review and rewrite hard-coded CUDA_VISIBLE_DEVICES and dataset folders before running.

References and Helpers

• references/data-layouts.md details local files expected for CSV, M4, anomaly, and UEA loaders.
• references/cli-and-outputs.md explains required flags, GPU controls, setting strings, and output folders.
• references/troubleshooting.md maps common path, parser, download, and output problems to fixes.
• scripts/validate_tslib_data.py performs file/header/layout checks without training or downloading data.
• ../../references/cli-arguments.md is the shared root CLI reference.
• ../../references/data-formats.md is the shared cross-task data-format reference.

Avoid

• Do not run full benchmark shell scripts as a preflight. Render a smaller command or use the forecasting command builder.
• Do not assume a missing local file is harmless; it can trigger network fallback and fail with a misleading Hub error.
• Do not let CUDA defaults choose the device for a smoke test; add --no_use_gpu unless GPU behavior is the point of the test.
• Do not treat tiny synthetic data as benchmark evidence. It validates plumbing only.