timeseries-wavelet-denoising
timeseries-wavelet-denoising is a Time series & forecasting skill for AI agents, published by wenmin-wu in the ds-skills repository on GitHub. Skillspace rates timeseries-wavelet-denoising D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Denoise an erratic 1D series with discrete wavelet decomposition + universal soft thresholding (sigma estimated from MAD of the detail coefficients) to extract the underlying trend/seasonality without lagging the signal — a far better trend extractor than rolling means for spiky retail or sensor data
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 52
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Portable: works in any project. General wavelet denoising technique for any 1D series
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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