Generate simulated changepoint data

Creates a synthetic time series with known changepoints for testing and benchmarking.

Usage

cpt_simulate(
  n,
  changepoints = integer(),
  change_in = c("mean", "var", "meanvar", "slope"),
  params = NULL,
  noise = c("gauss", "t", "ar1", "rw"),
  sd = 1,
  df = 3,
  rho = 0,
  seed = NULL
)

rcpt(...)

Arguments

n

Length of the series.

changepoints

Integer vector of changepoint locations (last index of each segment before the change).

change_in

What changes: "mean", "var", "meanvar", or "slope".

params

A list of parameters per segment. For mean changes, a vector of segment means. For var changes, a vector of segment sds. For meanvar, a list of lists with mean and sd per segment. For slope, a list with intercept and slope per segment.

noise

Noise type: "gauss" (Gaussian), "t" (Student-t), "ar1" (AR(1)), or "rw" (random walk).

sd

Noise standard deviation (for Gaussian and t; t-noise is rescaled so its standard deviation is exactly sd). Defaults to 1.

df

Degrees of freedom for t-noise; must exceed 2 so the variance exists. Defaults to 3.

rho

AR(1) autocorrelation parameter. Defaults to 0.

seed

Optional seed for reproducibility.

...

Passed to cpt_simulate.

Value

A tibble with columns index, value, and seg_id. The true changepoints are stored in the true_changepoints attribute.

Examples

dat <- cpt_simulate(200, changepoints = c(100), change_in = "mean",
                    params = c(0, 10), seed = 2022)
attr(dat, "true_changepoints")
#> [1] 100