JuliaDynamics / JuliaDynamics/RecurrenceAnalysis.jl
adaptive recurrence threshold specification
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- Dominant language
- Julia
- Stars
- 50
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
Description
In a very recent paper it is suggested to obtain an adaptive recurrence threshold based on local maximum attractor densities/distances. Basically, the distance matrix gets scanned using a sliding window (window size is a free parameter, but can be related to the decorrelation time of the time series) and for each window the maximum distance is picked. In turn, this local maximum distance will be used to scale the recurrence threshold accordingly.
The paper and the shown results sound promising and the effort to implement it (and also the computational complexity itself) seems to be manageable I suppose.
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 linked paper and comparing its adaptive threshold method with the repository's existing recurrence-threshold and distance-matrix entry points; no files or tests are named in the issue. Define the sliding-window behavior, window-size parameter, decorrelation-time relationship, and scaling rule, then validate the implementation against the paper's reported results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 28/100