fastcpd_ts: Find change points efficiently in time series data

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in time series data

Description

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

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)
plot(result)

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