scanCP
Deep Learning–Based Changepoint Detection with Local Neural Models
Implementation of deep learning–based changepoint detection algorithm designed for time series with smooth local fluctuations. The method fits localized feed‑forward neural networks to approximate the underlying smooth component and constructs a residual‑based detector that isolates abrupt structural changes. A fully data‑adaptive empirical cumulative distribution function (ECDF) based thresholding rule and refinement procedures yield accurate changepoint localization without parametric assumptions on noise or trend structure.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | scanCP_0.1.0.tar.gz |
81.8 KiB |
0.1.0 |
rolling linux/noble R-4.5 | scanCP_0.1.0.tar.gz |
81.7 KiB |
0.1.0 |
rolling source/ R- | scanCP_0.1.0.tar.gz |
21.0 KiB |
0.1.0 |
latest linux/jammy R-4.5 | scanCP_0.1.0.tar.gz |
81.8 KiB |
0.1.0 |
latest linux/noble R-4.5 | scanCP_0.1.0.tar.gz |
81.7 KiB |
0.1.0 |
latest source/ R- | scanCP_0.1.0.tar.gz |
21.0 KiB |
0.1.0 |
2026-04-23 source/ R- | scanCP_0.1.0.tar.gz |
0 B |