BinSeg {eNchange} | R Documentation |

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

```
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
)
```

`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 |

`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"). |

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

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

```
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]