DynamicsAndNeuralSystems / DynamicsAndNeuralSystems/pyspi
Similarity or Distance - Higher is closer or lower is closer?
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- Dominant language
- Python
- Stars
- 256
- Forks
- 34
- PR merge metrics
- No merged PRs in 30d
Description
First of all, thanks for creating this awesome package.
I want to use it to evaluate the measures for finding related time series within industrial/building time series datasets.
For that, I have the following questions:
Is there a place where I can quickly gather whether the outcome of a measure is a similarity or a distance? In other words, whether something is more related if the number is higher (similarity) or more related if the metric is lower (distance).
The simplest example would be the differentiation between correlation and Euclidean distance of two time series. For related time series, one could expect the correlation to be high but the Euclidean distance to be low. When, for example, searching for the k most related time series throughout the different metrics implemented here, this information is of great interest.
I'm sorry if I missed that in the publication or the paper. Kindly refer me if there is information on that anywhere.
Contributor guide
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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
No file or test is named. Review the package's implemented measures and the publication or paper referenced in the issue to determine which outputs are similarities and which are distances; done means users have a clear place to identify whether higher or lower values indicate closer relationships.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100