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name: foundation-models-and-customization description: "Navigate Time-Series-Library model files, optional Mamba and large time-series model dependencies, augmentation flags, and custom model or script additions." disable-model-invocation: true metadata: disco-role: operating license: MIT

TSLib Models and Customization

Use this sub-skill when the task is about choosing a TSLib model, diagnosing optional model dependencies, using zero-shot foundation models or Mamba variants, adding a new models/<Name>.py, adapting benchmark scripts, or using the built-in augmentation flags.

Route Here

• Explain how Exp_Basic discovers model files and how --model <Name> maps to models/<Name>.py.
• Choose a core smoke-test model versus an optional dependency-heavy model.
• Diagnose missing imports for mamba_ssm, chronos, timesfm, uni2ts, tirex, or transformers.
• Add or adapt a custom TSLib model file and create matching benchmark/smoke commands.
• Use augmentation flags such as --jitter, --scaling, --permutation, --timewarp, --wdba, or DTW-guided variants.
• Follow contribution expectations for new published-paper model additions.

Reroute

• Forecast command construction, TimeXer recipes, or zero-shot task commands: use ../forecasting/SKILL.md.
• Data layouts, run.py flag basics, GPU flags, and output folders: use ../data-and-cli/SKILL.md.
• Imputation/anomaly/classification task-specific recipes: use ../imputation-anomaly-classification/SKILL.md.

Start Fast

Inventory safe core and optional model imports before recommending a model:

python scripts/inspect_tslib_models.py --repo-root . --models DLinear TimesNet TimeXer PatchTST --optional-models

For smoke tests, prefer DLinear, TimesNet, TimeXer, PatchTST, or Transformer before Mamba/LTSM files. Optional model import failures should be treated as routing information, not as a broken core TSLib install.

Custom Model Contract

A model file normally lives at models/<Name>.py and exposes either Model or a class named <Name>. Most task models implement:

class Model(nn.Module):
    def __init__(self, configs): ...
    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): ...

Exp_Basic scans models/ dynamically at runtime, so adding a Python file is enough for discovery if imports and the class contract are valid.

References and Helpers

• references/model-catalog-and-dependencies.md maps model families to dependency and backend surfaces.
• references/customization.md covers custom model/script addition and augmentation usage.
• references/troubleshooting.md covers optional dependency, model import, shape, and source-tree problems.
• scripts/inspect_tslib_models.py probes model imports and class availability without downloading data or training.
• ../../references/model-catalog.md is the shared catalog used by the root skill.

Avoid

• Do not install all optional model stacks just because one import failed. First confirm the requested model family.
• Do not claim zero-shot, Mamba, or remote-code model execution is verified unless that exact package, model cache, and device path has been run.
• Do not add a model file without a small CPU-compatible shape/import smoke when possible.
• Do not copy upstream benchmark shell scripts without adjusting GPU ids, data paths, and epochs for the user's environment.