---

name: timemixer description: "Use TimeMixer to inspect the model API, build forecasting or non-forecast commands, validate dataset layouts, and debug shape or task-specific workflow issues." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TimeMixer

Use this repo skill for the TimeMixer time-series repository: model inspection, forecasting command construction, dataset layout validation, and the imputation/anomaly/classification branches.

Start here

1. Install the runtime dependencies from requirements.txt in a Python environment that can import the source checkout.

   • The repository notes a Python 3.8 sktime compatibility pin (sktime==0.29.1) if you are reproducing the historical runtime exactly.
   • The repository is source-first rather than a packaged distribution, so the bundled helpers add the checkout to sys.path instead of depending on a wheel install.

2. Keep helper invocation and source execution roots distinct. The helper files live under the skill directory, while run.py and the source packages live at the checkout root. From any shell, use explicit absolute paths:

   CHECKOUT_ROOT=/path/to/TimeMixer-checkout
   SKILL_ROOT="$CHECKOUT_ROOT/skills/disco/timemixer"
   cd "$CHECKOUT_ROOT"
   python "$SKILL_ROOT/scripts/check_timemixer_environment.py" --repo-root "$CHECKOUT_ROOT"
   

   Generated run.py commands must likewise be executed with cwd CHECKOUT_ROOT; --repo-root . is only correct when the current directory is the source checkout, never when it is the skill directory.

3. Then route the task to the smallest matching sub-skill.

Route map

If the user asks for...	Use
Model construction, forward shapes, decomposition choices, downsampling, or classification shape debugging	sub-skills/model-architecture/SKILL.md
Forecasting benchmark commands, custom forecast adaptation, checkpoints, or M4/PEMS/ECL/Weather/Solar recipes	sub-skills/forecasting-experiments/SKILL.md
Dataset file layouts, CSV/NPY/TS validation, time-feature conventions, or benchmark data placement	sub-skills/data-preparation/SKILL.md
Imputation, anomaly detection, or classification workflows and task-specific command construction	sub-skills/universal-tasks/SKILL.md

Quick reference files

• Read references/cli-reference.md when you need the run.py argument groups or task-to-flag mapping.
• Read references/troubleshooting.md for install/import failures, the run.py --help percent-format bug, or other cross-cutting setup problems.
• Read references/repo-provenance.md when you need to check whether this skill matches a different TimeMixer checkout or before refreshing the skill.
• Read references/repo-routing-metadata.json only as managed-router metadata; it is not a user guide.

Safe checks

• Use scripts/check_timemixer_environment.py to confirm that the checkout can be imported from a clean source-root path.
• Use sub-skills/model-architecture/scripts/smoke_timemixer_forward.py when you need a tiny CPU-only forward shape smoke.
• Use the forecasting or universal-task command builders before launching any long experiment.

What this skill covers

• TimeMixer.Model architecture and forward behavior.
• Long-term and short-term forecast command construction.
• Custom dataset and benchmark data validation.
• Imputation, anomaly detection, and classification task branches.
• Common run.py, dataset, and tensor-shape failure modes.

What it does not do

• It does not reproduce benchmark-scale training runs.
• It does not download datasets for you.
• It does not depend on the original source checkout being opened through the skill files themselves; the bundled references and scripts summarize the needed behavior.

Notes for future agents

• run.py --help is unreliable because one help string contains an unescaped percent sign.
• Prefer the bundled command builders for safe command generation.
• Keep the root skill as a router; deeper API, workflow, and troubleshooting detail lives in the sub-skills.