kcpa: KCP (Kernel Change Point) Detection

In kcpRS: Kernel Change Point Detection on the Running Statistics

KCP (Kernel Change Point) Detection

Description

Finds the most optimal change point(s) in the running statistic time series RunStat by looking at their kernel-based pairwise similarities.

Usage

kcpa(RunStat, Kmax = 10, wsize = 25)

Arguments

RunStat	Dataframe of running statistics with rows corresponding to the windows and the columns corresponding to the variable(s) on which these running statistics were computed.
Kmax	Maximum number of change points
wsize	Window size

Value

kcpSoln

A matrix comprised of the minimized variance criterion Rmin and the optimal change point location(s) for each k from 1 to Kmax

References

Arlot, S., Celisse, A., & Harchaoui, Z. (2019). A kernel multiple change-point algorithm via model selection. Journal of Machine Learning Research, 20(162), 1-56.

Cabrieto, J., Tuerlinckx, F., Kuppens, P., Grassmann, M., & Ceulemans, E. (2017). Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Behavior Research Methods, 49, 988-1005. doi:10.3758/s13428-016-0754-9

kcpRS documentation built on Oct. 25, 2023, 5:07 p.m.