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name: analysis-and-persistence description: "MatrixProfile, STUMPY-backed alternatives, and estimator round-tripping via JSON, Pickle, and HDF5." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

analysis-and-persistence

Use this sub-skill for tslearn.matrix_profile and persistence work.

Stay here when the task asks you to:

• compare MatrixProfile implementations (numpy, stump, optional gpu_stump)
• serialize or reload fitted tslearn estimators with BaseModelPackage
• reason about tslearn.hdftools and HDF5-backed round-trips

Route elsewhere when the task is mainly about:

• metric derivations or backend selection: metrics-backends
• clustering workflows: clustering
• supervised learning: supervised-models
• forecasting: forecasting
• data loading, scaling, or feature prep: data-preparation

Rules of thumb:

• Treat numpy as the reference path for MatrixProfile correctness.
• Treat stump as the CPU acceleration path that should match numpy on the same tiny univariate series when stumpy is installed.
• Treat gpu_stump as optional acceleration only; do not make it the baseline check.
• Use JSON and Pickle without h5py.
• Use HDF5 only when h5py is installed.
• Expect unfitted estimators to raise NotFittedError on to_*.
• Keep matrix-profile inputs univariate for the bundled checks.

Start with:

• API reference
• Workflows
• Troubleshooting
• Smoke script

If you need to expand beyond this scope, hand off through the root router.