tslearn-team / tslearn-team/tslearn
Feature Importance/Influence in Multivariate Time Series Clustering
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new feature
- Dominant language
- Python
- Stars
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- Forks
- 384
- Avg merge
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- Merged PRs (30d)
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Description
Is there a way to determine the importance of each features in multivariate time series for the decision of the clustering? For example, feature x has the most influence in cluster y.
My time series is modeled as (n_ts, ts_length, n_dim) with n_dim as the number of features.
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.
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Research direction
The issue provides no file, test, or entry point to start from. Review tslearn's existing multivariate time-series clustering functionality and determine how feature influence would be defined for a cluster. Done should include an agreed method for identifying feature importance and corresponding documentation or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 18/100