kernelCPA: Kernel Change Point Analysis

In ecp: Non-Parametric Multiple Change-Point Analysis of Multivariate Data

Kernel Change Point Analysis

Description

An algorithm for multiple change point analysis that uses the 'kernel trick' and dynamic programming.

Usage

kcpa(X, L, C)

Arguments

X	A T x d matrix containing the length T time series with d-dimensional observations.
L	The maximum number of change points.
C	The constant used to penalize the inclusion of additional change points in the fitted model.

Details

Segments are found through the use of dynamic programming and the kernel trick.

Value

If the algorithm determines that the best fit is obtained through using k change points then the returned value is an array of length k, containing the change point locations.

Author(s)

Nicholas A. James

References

Arlot S., Celisse A., Harchaoui Z. (2019). A Kernel Multiple Change-point Algorithm via Model Selection. J. Mach. Learn. Res., 20, 162:1-162:56.

ecp documentation built on Sept. 12, 2024, 7:40 a.m.