Z_trans {eNchange} | R Documentation |
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
.
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
)
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. |
Returns the transformed residual 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 <- 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))