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ocd wrapper — online high-dimensional changepoint detection

Source: R/wrap-highdim.R
ocd_wrapper.Rd

Wraps the ocd package (Chen, Wang and Samworth, 2022): online multiscale detection of a mean change in a high-dimensional stream, with worst-case detection-delay guarantees and per-observation cost independent of history. The detector assumes standardised data with known pre-change mean; this wrapper estimates the baseline mean and standard deviation from an initial training window, then monitors the remainder of the series, resetting after each declaration so multiple changes can be found.

Usage

ocd_wrapper(
  x,
  train = NULL,
  thresh = "MC",
  patience = 5000,
  beta = 1,
  mc_reps = 100,
  ...
)

Arguments

x

A numeric matrix (rows are time points) or vector.

train

Number of initial observations used to estimate the baseline mean/sd (not monitored). Defaults to max(20, floor(0.2 * n)), capped at n/2.

thresh

Threshold specification passed to ocd::ChangepointDetector(); "MC" (default) calibrates by Monte Carlo.

patience

Target average run length to false alarm. Defaults to 5000.

beta

Assumed lower bound on the squared Euclidean norm of the mean change. Defaults to 1.

mc_reps

Monte Carlo repetitions for threshold calibration. Defaults to 100.

...

Additional arguments passed to ocd::ChangepointDetector().

Value

A ggcpt object. Because the detector is online, reported locations are declaration times (the changepoint plus the detection delay), stored together with a declared_at column.

References

Chen Y, Wang T, Samworth RJ (2022). “High-dimensional, multiscale online changepoint detection.” Journal of the Royal Statistical Society: Series B, 84(1), 234–266.

Examples

# \donttest{
set.seed(2026)
X <- rbind(matrix(rnorm(100 * 5), 100), matrix(rnorm(60 * 5, 2), 60))
res <- ocd_wrapper(X, mc_reps = 20)
res$changepoints
#> # A tibble: 2 × 3
#>      cp cp_value declared_at
#>   <int>    <dbl>       <int>
#> 1   101     3.17         101
#> 2   158     1.20         158
# }