eNchange (version 1.0)

eNchange-package: Ensemble Methods for Multiple Change-Point Detection

Description

Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2020) <arXiv:2003.03649>.

Details

We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the Ensemble Binary Segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the Binary Segmentation (BS) method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. This methodology is applied to irregularly time series models such as the time-varying Autoregressive Conditional Duration model or the time-varying Hawkes process.

References

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

Examples

# NOT RUN {
  
# }
# NOT RUN {
 pw.acd.obj <- new("simACD")
 [e-mail] <- seq(0.1,0.95,by=0.025)
 pw.acd.obj@lambda_0 <- rep(c(0.5,2),1+length([e-mail])/2)
 pw.acd.obj@alpha <- rep(0.2,1+length([e-mail]))
 pw.acd.obj@beta <- rep(0.4,1+length([e-mail]))
 pw.acd.obj@N <- 5000
 pw.acd.obj <- pc_acdsim(pw.acd.obj)
 ts.plot(pw.acd.obj@x,main="Ensemble BS");abline(v=EnBinSeg(pw.acd.obj@x)[[1]],col="red")
 #real change-points in grey
 abline(v=floor([e-mail]*pw.acd.obj@N),col="grey",lty=2) 
 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([e-mail]*pw.acd.obj@N),col="grey",lty=2)

  
# }