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motifs-anomalies-segmentation

motifs-anomalies-segmentation

Route matrix-profile results into motif discovery, query matching, discords/anomalies, consensus motifs, MPdist, snippets, chains, shapelets, and semantic segmentation.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: motifs-anomalies-segmentation description: "Route matrix-profile results into motif discovery, query matching, discords/anomalies, consensus motifs, MPdist, snippets, chains, shapelets, and semantic segmentation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Motifs, anomalies, and segmentation

Use this sub-skill when the matrix profile already exists, or when you already know the query or comparison series and need analysis rather than profile construction.

Route here for

  • motif extraction from a 1-D profile
  • query matching against a raw series
  • discords / anomalies from profile peaks
  • consensus motifs across a list of series
  • MPdist and snippets for similarity and summarization
  • chains from left / right nearest-neighbor indices
  • FLUSS / FLOSS semantic segmentation
  • shapelet candidate ranking from class-vs-class profile contrasts

Route away when

  • you still need the base 1-D profile computation -> matrix-profile-basics
  • you need multidimensional profile construction or subspace choice -> multidimensional-profiles
  • you need online, anytime, or pan updates -> approximate-streaming-pan
  • you need Dask / GPU / Ray acceleration setup -> distributed-gpu-acceleration

What the APIs consume

  • raw series + 1-D profile -> motifs
  • raw query + raw series -> match
  • list of raw series -> ostinato
  • precomputed multidimensional profile / index arrays -> mmotifs handoff only
  • raw pair of series -> mpdist
  • raw series -> snippets
  • left / right index columns -> atsc, allc
  • nearest-neighbor index column -> fluss
  • full 4-column matrix profile + raw series -> floss

Workflow

  1. Start from the smallest stable input you already have.
  2. Use references/workflows.md for the exact path.
  3. Use scripts/motif_segmentation_smoke.py --help for the bundled no-network smoke path.
  4. If the failure is really about profile creation, dimensionality, or acceleration, hand off to the owning sub-skill.

Validation cues

  • low profile values are motif candidates
  • high profile values are discord / anomaly candidates
  • normalize=False must stay consistent across the upstream profile and the downstream analysis call
  • atsc, allc, and fluss are index-driven; do not pass profile distances where indices are required

Bundled references

  • references/api-reference.md
  • references/workflows.md
  • references/troubleshooting.md
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