Z_trans-methods: Transformation of an irregularly spaces time series.

In eNchange: Ensemble Methods for Multiple Change-Point Detection

Description

Transformation of a irregularly spaces time series. For the tvACD model, we calculate U_t = g_0(x_t, ψ_t) = \frac{x_t}{{ψ}_t}, where {ψ}_t = C_0 + ∑_{j=1}^p C_j x_{t-j} + ∑_{k=1}^q C_{p+k} ψ_{t-k}+ε x_t. where the last term ε 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")
[e-mail] <- 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))

eNchange documentation built on March 26, 2020, 5:52 p.m.