kcpa

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

Kernel

Implements various procedures for finding
multiple change-points from Matteson D. et al (2013)
<doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017)
<doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019).
Two methods make use of dynamic
programming and pruning, with no distributional
assumptions other than the existence of certain absolute
moments in one method. Hierarchical and exact search methods
are included. All methods return the set of estimated change-
points as well as other summary information.

Wenyu Zhang

Non-Parametric Multiple Change-Point Analysis of Multivariate
Data

kcpa function

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

Arguments

Author

Kernel Change Point Analysis — kcpa

<dl>

 <dt>X</dt>
<dd>A T x d matrix containing the length T time series with d-dimensional observations.</dd>

 <dt>L</dt>
<dd>The maximum number of change points.</dd>

 <dt>C</dt>
<dd>The constant used to penalize the inclusion of additional change points in the fitted model.</dd>

</dl>

kcpa: Kernel Change Point Analysis

Description

Usage

Value

Arguments

Author

Details

References