BinSeg {eNchange} R Documentation

## An S4 method to detect the change-points in an irregularly spaced time series using Binary Segmentation.

### Description

An S4 method to detect the change-points in an irregularly spaced time series using the Binary Segmentation methodology described in Korkas (2020).

### Usage

BinSeg(
H,
thresh = "universal",
q = 0.99,
p = 1,
z = NULL,
start.values = c(0.9, 0.6),
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
process = "acd",
acd_p = 0,
acd_q = 1,
do.parallel = 2
)

## S4 method for signature 'ANY'
BinSeg(
H,
thresh = "universal",
q = 0.99,
p = 1,
z = NULL,
start.values = c(0.9, 0.6),
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
process = "acd",
acd_p = 0,
acd_q = 1,
do.parallel = 2
)


### Arguments

 H The input irregular time series. thresh The threshold parameter which acts as a stopping rule to detect further change-points and has the form C log(sample). If "universal" then C is data-independent and preselected using the approach described in Korkas (2020). If "boot" it uses the data-dependent method boot_thresh. Default is "universal". q The universal threshold simulation quantile or the bootstrap distribution quantile. Default is 0.99. p The support of the CUSUM statistic. Default is 1. z Transform the time series to use for post-processing. If NULL this is done automatically. Default is NULL. start.values Warm starts for the optimizers of the likelihood functions. dampen.factor The dampen factor in the denominator of the residual process. Default is "auto". epsilon A parameter added to ensure the boundness of the residual process. Default is 1e-5. LOG Take the log of the residual process. Default is TRUE. process Choose between acd or hawkes. Default is acd. acd_p The p order of the ACD model. Default is 0. acd_q The q order of the ACD model. Default is 1. do.parallel Choose the number of cores for parallel computation. If 0 no parallelism is done. Default is 2. (Only applies if thresh = "boot").

### Value

Returns a list with the detected change-points and the transformed series.

### References

Korkas Karolos. "Ensemble Binary Segmentation for irregularly spaced data with change-points" Preprint <arXiv:2003.03649>.

### Examples

pw.acd.obj <- new("simACD")
pw.acd.obj@cp.loc <- seq(0.1,0.95,by=0.025)
pw.acd.obj@lambda_0 <- rep(c(0.5,2),1+length(pw.acd.obj@cp.loc)/2)
pw.acd.obj@alpha <- rep(0.2,1+length(pw.acd.obj@cp.loc))
pw.acd.obj@beta <- rep(0.4,1+length(pw.acd.obj@cp.loc))
pw.acd.obj@N <- 5000
pw.acd.obj <- pc_acdsim(pw.acd.obj)
ts.plot(pw.acd.obj@x,main="Standard BS");abline(v=BinSeg(pw.acd.obj@x)[[1]],col="blue")
#real change-points in grey
abline(v=floor(pw.acd.obj@cp.loc*pw.acd.obj@N),col="grey",lty=2)


[Package eNchange version 1.0 Index]