Find change points efficiently in MA(\(q\)) models

fastcpd_ma() and fastcpd.ma() are wrapper functions of fastcpd() to find change points in MA(\(q\)) 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_ma(data, order = 0, ...)

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

Arguments

data

A numeric vector, a matrix, a data frame or a time series object.

order

A positive integer or a vector of length three with the first two elements being zeros specifying the order of the MA 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

# \donttest{
set.seed(1)
n <- 400
w <- rnorm(n + 4, 0, 0.1)
x <- rep(NA, n)
for (i in 1:200) {
  x[i] <- w[i + 4] - 5 / 3 * w[i + 3] + 11 / 12 * w[i + 2] - 5 / 12 * w[i + 1] +
    1 / 6 * w[i]
}
for (i in 201:n) {
  x[i] <- w[i + 4] - 4 / 3 * w[i + 3] + 7 / 9 * w[i + 2] - 16 / 27 * w[i + 1] +
    4 / 27 * w[i]
}
result <- suppressMessages(
  fastcpd.ma(x, 4, include.mean = FALSE, trim = 0)
)
summary(result)
#> 
#> Call:
#> fastcpd.ma(data = x, order = 4, include.mean = FALSE, trim = 0)
#> 
#> Change points:
#> 200 
#> 
#> Cost values:
#> -185.4279 -174.3401 
plot(result)

# }