StructuralDecompose - Decomposes a Level Shifted Time Series
Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.
Last updated
decompositiontimeseries-analysis
5.18 score 2 stars 10 scripts 239 downloads
TangledFeatures - Feature Selection in Highly Correlated Spaces
Feature selection algorithm that extracts features in highly correlated spaces. The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior.
Last updated
documentationneurips-2025
3.00 score 10 scripts 205 downloads