---

name: multivariate-workflows description: "Routes pyts multivariate transformer, classifier, image, and validation workflows for 3D time-series inputs." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

multivariate-workflows

Use this sub-skill when the input has a feature/channel axis and the task is to wrap a univariate pyts estimator for multivariate data or use a multivariate specific transform.

What this covers

• MultivariateTransformer for wrapping univariate transformers.
• MultivariateClassifier for wrapping univariate classifiers.
• WEASELMUSE for multivariate symbolic feature extraction.
• JointRecurrencePlot for multivariate recurrence images.
• check_3d_array for shape validation.

What this excludes

• Univariate dataset loading: use ../datasets-and-loaders/SKILL.md.
• Univariate preprocessing and symbolic encoders: use ../preprocessing-and-symbols/SKILL.md.
• Univariate feature extraction and image transforms: use ../feature-extraction-and-images/SKILL.md.
• Univariate metrics and classifiers: use ../metrics-and-classifiers/SKILL.md.

Start here

1. Read references/workflows.md for the multivariate recipes and shape expectations.
2. Read references/api-reference.md for the verified signatures.
3. Read references/troubleshooting.md for 2D-vs-3D and flattening problems.
4. Run scripts/smoke.py to confirm the installed package on a tiny multivariate case.

Useful triggers

• "wrap a transformer for multivariate time series"
• "BasicMotions with pyts"
• "multivariate recurrence plot"
• "WEASELMUSE"
• "why does my input need to be 3D?"

Routing hints

• If the user has a 2D array but mentions multiple channels, clarify the intended axis order before modeling.
• If the user needs a multivariate image or classifier, route here even when the underlying estimator is a univariate pyts object.
• If the user needs a downstream classifier after multivariate transformation, keep the wrapper choice in this sub-skill and then hand off to the model the wrapper contains.

Links

• Read references/workflows.md for the canonical 3D input recipes.
• Read references/api-reference.md for the verified signatures and defaults.
• Read references/troubleshooting.md for shape and output-size pitfalls.
• Run scripts/smoke.py for a quick installed-package check.