fastcpd_var: Find change points efficiently in VAR(p) models

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in VAR(p) models

Description

fastcpd_var() and fastcpd.var() are wrapper functions of fastcpd_ts() to find change points in VAR(p) models. The function is similar to fastcpd_ts() except that the data is by default a matrix with row as an observation and thus a formula is not required here.

Usage

fastcpd_var(data, order = 0, ...)

fastcpd.var(data, order = 0, ...)

Arguments

data	A matrix, a data frame or a time series object.
order	A positive integer specifying the order of the VAR model.
...	Other arguments passed to fastcpd(), for example, segment_count.

Value

A fastcpd object.

See Also

fastcpd()

Examples

set.seed(1)
n <- 300
p <- 2
theta_1 <- matrix(c(-0.3, 0.6, -0.5, 0.4, 0.2, 0.2, 0.2, -0.2), nrow = p)
theta_2 <- matrix(c(0.3, -0.4, 0.1, -0.5, -0.5, -0.2, -0.5, 0.2), nrow = p)
x <- matrix(0, n + 2, p)
for (i in 1:200) {
  x[i + 2, ] <- theta_1 %*% c(x[i + 1, ], x[i, ]) + rnorm(p, 0, 1)
}
for (i in 201:n) {
  x[i + 2, ] <- theta_2 %*% c(x[i + 1, ], x[i, ]) + rnorm(p, 0, 1)
}
result <- fastcpd.var(x, 2)
summary(result)

fastcpd documentation built on May 29, 2024, 8:36 a.m.