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Find change points efficiently in time series data

Source: R/fastcpd_wrappers.R
fastcpd_ts.Rd

fastcpd_ts() and fastcpd.ts() are wrapper functions for fastcpd() to find change points in time series data. The function is similar to fastcpd() except that the data is a time series and the family is one of "ar", "var", "arma", "arima" or "garch".

Usage

fastcpd_ts(data, family = NULL, order = c(0, 0, 0), ...)

fastcpd.ts(data, family = NULL, order = c(0, 0, 0), ...)

Arguments

data

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

family

A character string specifying the family of the time series. The value should be one of "ar", "var", "arima" or "garch".

order

A positive integer or a vector of length less than four specifying the order of the time series. Possible combinations with family are:

  • "ar", NUMERIC(1): AR(\(p\)) model using linear regression.

  • "var", NUMERIC(1): VAR(\(p\)) model using linear regression.

  • "arima", NUMERIC(3): ARIMA(\(p\), \(d\), \(q\)) model using forecast::Arima().

  • "garch", NUMERIC(2): GARCH(\(p\), \(q\)) model using tseries::garch().

...

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 <- fastcpd.ts(
  x,
  "arma",
  c(0, 4),
  lower = c(-2, -2, -2, -2, 1e-10),
  upper = c(2, 2, 2, 2, Inf),
  line_search = c(1, 0.1, 1e-2),
  trim = 0.05
)
summary(result)
#> 
#> Call:
#> fastcpd.ts(data = x, family = "arma", order = c(0, 4), lower = c(-2, 
#>     -2, -2, -2, 1e-10), upper = c(2, 2, 2, 2, Inf), line_search = c(1, 
#>     0.1, 0.01), trim = 0.05)
#> 
#> Change points:
#> 209 
#> 
#> Cost values:
#> -188.7341 -168.4595 
#> 
#> Parameters:
#>      segment 1    segment 2
#> 1 -1.626949633 -1.366413227
#> 2  0.925842329  0.850647424
#> 3 -0.472212640 -0.677337192
#> 4  0.181290578  0.193108171
#> 5  0.009523233  0.009914231
plot(result)

# }