fastcpd_ar: Find change points efficiently in AR(p) models

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in AR(p) models

Description

fastcpd_ar() and fastcpd.ar() are wrapper functions of fastcpd() to find change points in AR(p) models. The function is similar to fastcpd() except that the data is by default a one-column matrix or univariate vector and thus a formula is not required here.

Usage

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

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

Arguments

data	A numeric vector, a matrix, a data frame or a time series object.
order	A positive integer specifying the order of the AR model.
...	Other arguments passed to fastcpd(), for example, segment_count. One special argument can be passed here is include.mean, which is a logical value indicating whether the mean should be included in the model. The default value is TRUE.

Value

A fastcpd object.

See Also

fastcpd()

Examples

set.seed(1)
n <- 1000
x <- rep(0, n + 3)
for (i in 1:600) {
  x[i + 3] <- 0.6 * x[i + 2] - 0.2 * x[i + 1] + 0.1 * x[i] + rnorm(1, 0, 3)
}
for (i in 601:1000) {
  x[i + 3] <- 0.3 * x[i + 2] + 0.4 * x[i + 1] + 0.2 * x[i] + rnorm(1, 0, 3)
}
result <- fastcpd.ar(x[3 + seq_len(n)], 3)
summary(result)
plot(result)

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