tslearn-team / tslearn-team/tslearn
tuning gamma for tslearn.svm.TimeSeriesSVC - Question
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 384
- Avg merge
- 3d 12h
- Merged PRs (30d)
- 11
Description
Hello,
I have a small question about classifying multivariate time series using SVM.
In tslearn, there is an option to classify time series or multivariate time series using tslearn.svm.TimeSeriesSVC. Here, according to the documentation, "gak" kernel is used. Now I haven't seen anywhere a detailed explanation how actually the parameter gamma works with this kernel "GAK". Should it always stay in the default value "auto"?
Because I have used other gamma parameters like (0.1, 1, 10) but got very bad results compared to "auto". So I wanted to ask how the gamma works with this kernel and I haven't seen a formula anywhere. Or is there a specific article for this ?
Thanks in advance
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 with the documentation for tslearn.svm.TimeSeriesSVC and the GAK kernel, then trace how the gamma parameter is defined or passed through the implementation. Done would be a clear explanation of gamma for GAK, including its formula or an authoritative reference and guidance on the auto default; the issue does not name a file or test to update.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100