scikit-learn / scikit-learn/scikit-learn
Add Matrix Profile algorithm to scikit-learn
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 67.3k
- Forks
- 27.4k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 58
Description
Matrix Profile is an algorithm developed by the Keogh research group at UC-Riverside (https://www.cs.ucr.edu/~eamonn/MatrixProfile.html) that serves as an extremely powerful tool for finding patterns, anomalies and evolving behaviors in massive timeseries datasets. We released a Python version of Matrix Profile last year (matrixprofile-ts) and it seems like a good idea to align further development with Python machine learning best practices, as well as to broaden the user/developer base.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the Matrix Profile research page and the existing matrixprofile-ts Python implementation, then compare its scope with scikit-learn’s machine-learning conventions. The issue does not name files, tests, an entry point, or concrete acceptance criteria; done would require agreeing on how the algorithm should be integrated and validated.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100