fastcpd_arima: Find change points efficiently in ARIMA(p, d, q) models

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in ARIMA(p, d, q) models

Description

fastcpd_arima() and fastcpd.arima() are wrapper functions of fastcpd() to find change points in ARIMA(p, d, q) 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_arima(data, order = 0, ...)

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

Arguments

data	A numeric vector, a matrix, a data frame or a time series object.
order	A vector of length three specifying the order of the ARIMA 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 <- 271
w <- rnorm(n + 1, 0, 3)
dx <- rep(0, n + 1)
x <- rep(0, n + 1)
for (i in 1:180) {
  dx[i + 1] <- 0.8 * dx[i] + w[i + 1] - 0.5 * w[i]
  x[i + 1] <- x[i] + dx[i + 1]
}
for (i in 181:n) {
  dx[i + 1] <- -0.6 * dx[i] + w[i + 1] + 0.3 * w[i]
  x[i + 1] <- x[i] + dx[i + 1]
}
result <- fastcpd.arima(
  diff(x[1 + seq_len(n)]),
  c(1, 0, 1),
  segment_count = 3,
  include.mean = FALSE
)
summary(result)
plot(result)

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