simACD-class: An S4 class for a nonstationary ACD model.
In eNchange: Ensemble Methods for Multiple Change-Point Detection

Description Value Slots References Examples

A specification class to create an object of a simulated piecewise constant conditional duration model of order (1,1). x_t / ψ_t = \varepsilon_t \; \sim \mathcal{G}(θ_2) ψ_t = ω(t) + ∑_{j=1}^p α_{j}(t)x_{t-j} + ∑_{k=1}^q β_{k}(t)ψ_{t-k}. where ψ_{t} = \mathcal{E} [x_t | x_t,…,x_1| θ_1] is the conditional mean duration of the t-th event with parameter vector θ_1 and \mathcal{G}(.) is a general distribution over (0,+∞) with mean equal to 1 and parameter vector θ_2. In this work we assume that \varepsilon_t \; \sim \exp(1).

Returns an object of simACD class.

x: The durational time series.
psi: The psi time series.
N: Sample sze of the time series.
cp.loc: The vector with the location of the changepoints. Takes values from 0 to 1 or NULL. Default is NULL.
lambda_0: The vector of the parameters lambda_0 in the ACD series as in the above formula.
alpha: The vector of the parameters alpha in the ACD series as in the above formula.
beta: The vector of the parameters beta in the ACD series as in the above formula.
BurnIn: The size of the burn-in sample. Note that this only applies at the first simulated segment. Default is 500.

Korkas Karolos. "Ensemble Binary Segmentation for irregularly spaced data with change-points" Preprint.

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 <- 3000
pw.acd.obj <- pc_acdsim(pw.acd.obj)
ts.plot(pw.acd.obj@x)
ts.plot(pw.acd.obj@psi)