ks.cp3o: CHANGE POINTS ESTIMATION BY PRUNED OBJECTIVE (VIA...

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

CHANGE POINTS ESTIMATION BY PRUNED OBJECTIVE (VIA KOLMOGOROV-SMIRNOV STATISTIC)

Description

An algorithm for multiple change point analysis that uses dynamic programming and pruning. The Kolmogorov-Smirnov statistic is used as the goodness-of-fit measure.

Usage

ks.cp3o(Z, K=1, minsize=30, verbose=FALSE)

Arguments

Z	A T x d matrix containing the length T time series with d-dimensional observations.
K	The maximum number of change points.
minsize	The minimum segment size.
verbose	A flag indicating if status updates should be printed.

Details

Segmentations are found through the use of dynamic programming and pruning. For long time series, consider using ks.cp3o_delta.

Value

The returned value is a list with the following components.

number	The estimated number of change points.
estimates	The location of the change points estimated by the procedure.
gofM	A vector of goodness of fit values for differing number of change points. The first entry corresponds to when there is only a single change point, the second for when there are two, and so on.
cpLoc	The list of locations of change points estimated by the procedure for different numbers of change points up to K.
time	The total amount to time take to estimate the change point locations.

Author(s)

Wenyu Zhang

References

W. Zhang, N. A. James and D. S. Matteson, "Pruning and Nonparametric Multiple Change Point Detection," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, pp. 288-295.

See Also

Kifer D., Ben-David S., Gehrke J. (2004). Detecting change in data streams. International Conference on Very Large Data Bases.

Examples

set.seed(400)
x = matrix(c(rnorm(100),rnorm(100,3),rnorm(100,0,2)))
y = ks.cp3o(Z=x, K=7, minsize=30, verbose=FALSE)
#View estimated change point locations
y$estimates

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