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在实现前澄清需求、探索设计选择,并形成清晰的开发方向。
model-architecture【开发与工程】Use and debug the TimeMixer Model API, PDM/FMM architecture, decomposition, downsampling, channel independence, normalization, and task-specific forward shapes.
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Use this sub-skill when the task is about instantiating or debugging the TimeMixer model class, checking forward-output shapes, choosing decomposition/downsampling/channel settings, or explaining the Past-Decomposable-Mixing and Future-Multipredictor-Mixing internals.
Model.forward(x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None) behavior.moving_avg versus dft_decomp, avg/max/conv downsampling, channel_independence, use_norm, and future temporal features.Read references/api-reference.md for the model constructor inputs, forward signature, and task output contracts.
Read references/architecture-notes.md before changing decomposition, downsampling, normalization, future temporal features, or channel independence.
Use references/troubleshooting.md to map common RuntimeErrors to concrete config or tensor fixes.
Run the bundled smoke helper when you need a fast CPU-only shape check. Invoke the helper by its explicit skill path, while pointing --repo-root at the source checkout:
cd /path/to/TimeMixer-checkout
python /path/to/timemixer-skill/sub-skills/model-architecture/scripts/smoke_timemixer_forward.py --repo-root /path/to/TimeMixer-checkout --task long_term_forecast
The smoke helper is diagnostic only. It does not train, download data, run benchmark scripts, or read generated test artifacts.