tslearn-team / tslearn-team/tslearn
Faster kNN search with constrained DTW
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new feature
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 384
- Avg merge
- 3d 12h
- Merged PRs (30d)
- 11
Description
It would be nice to make kNN searches faster when Sakoe-Chiba constrained DTW is concerned using LB_Keogh based pre-filtering.
This should be implemented in the kneighbors method of class KNeighborsTimeSeriesMixin from module neighbors.
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 reading the kneighbors method of KNeighborsTimeSeriesMixin in the neighbors module, along with the existing Sakoe-Chiba constrained DTW and LB_Keogh behavior. The work is complete when LB_Keogh pre-filtering is integrated there and kNN results remain correct while searches are faster.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100