ks.cp3o {ecp} | R Documentation |
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.
ks.cp3o(Z, K=1, minsize=30, verbose=FALSE)
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. |
Segmentations are found through the use of dynamic programming and pruning. For long time series, consider using ks.cp3o_delta.
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. |
Wenyu Zhang
Kifer D., Ben-David S., Gehrke J. (2004). Detecting change in data streams. International Conference on Very Large Data Bases.
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