tslearn-team / tslearn-team/tslearn
Implement softDTW divergences
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- Python
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Description
As discussed in #307, several similarity measures for time series (based on softDTW, namely "sharp softDTW" and "softDTW divergence") are introduced in [1] and it would make sense to make them available in tslearn.
As written in #307, "the heaviest piece of work there would be to have careful docs that would prevent end-users from getting confused about all these variants".
[1] Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert. Differentiable Divergences Between Time Series
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 discussion in issue #307 and reference [1], which introduces sharp softDTW and softDTW divergence. The work is complete when both measures are available in tslearn and the documentation clearly distinguishes these variants for end-users.
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
- Mostly clear
- Newbie friendliness
- 25/100