Z_trans {eNchange} R Documentation

## Transformation of an irregularly spaces time series.

### Description

Transformation of a irregularly spaces time series. For the tvACD model, we calculate U_t = g_0(x_t, \psi_t) = \frac{x_t}{{\psi}_t}, where {\psi}_t = C_0 + \sum_{j=1}^p C_j x_{t-j} + \sum_{k=1}^q C_{p+k} \psi_{t-k}+\epsilon x_t. where the last term \epsilon x_t is added to ensure the boundness of U_t.

### Usage

Z_trans(
H,
start.values = c(0.9, 0.6),
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
process = "acd",
acd_p = 0,
acd_q = 1
)

## S4 method for signature 'ANY'
Z_trans(
H,
start.values = c(0.9, 0.6),
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
process = "acd",
acd_p = 0,
acd_q = 1
)


### Arguments

 H The input irregular time series. 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-6. 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.

### Value

Returns the transformed residual 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 <- c(0.25,0.75)
pw.acd.obj@lambda_0 <- c(1,2,1)
pw.acd.obj@alpha <- rep(0.2,3)
pw.acd.obj@beta <- rep(0.7,3)
pw.acd.obj@N <- 1000
pw.acd.obj <- pc_acdsim(pw.acd.obj)
ts.plot(Z_trans(pw.acd.obj@x))


[Package eNchange version 1.0 Index]