Finds the most optimal change point(s) in the running statistic time series RunStat by
looking at their kernel-based pairwise similarities.
kcpa(RunStat, Kmax = 10, wsize = 25)A matrix comprised of the minimized variance criterion Rmin and the optimal change point location(s) for each k from 1 to Kmax
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.
Maximum number of change points
Window size
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