autocorrelation-and-lag-selection
autocorrelation-and-lag-selection is a Time series & forecasting skill for AI agents, published by skforecast in the skforecast repository on GitHub. Skillspace rates autocorrelation-and-lag-selection D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Analyzes time series dynamics with the fast skforecast.stats functions acf, pacf and calculate_lag_autocorrelation. Covers reading ACF/PACF patterns to identify AR/MA orders and seasonality, ranking lags by partial autocorrelation, and feeding the result to the lags argument of any skforecast forecaster. Use when the user wants to understand the dynamics of a series, choose a candidate set of lags before hyperparameter tuning, or replace a slow statsmodels acf/pacf call.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 1,512
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
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- BSD-3-Clause
- Adoption
- Adopted by 1 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Documents the public skforecast library's ACF/PACF and forecasters
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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