Invariances of every engine's answer

Five replicates of a 200-point series with changes at 70 and 140 (a 3-coordinate version for the multivariate engines), detected as given and transformed. A correct mean-change detector returns the same changepoints on 10 * x, 0.1 * x and x + 100, and n - cp on the reversed series.

Usage

cpt_invariances

Format

A tibble with one row per engine:

method

the method.

reps

replicates that ran.

k

median changepoints on the series as given.

scale_invariant

the same answer at x, 10 * x and 0.1 * x in at least four of five replicates. FALSE for the Gaussian-cost engines that assume unit noise, which is why cpt_detect() warns when one is handed noise far from unit scale.

shift_invariant

the same answer on x + 100.

reversal_rate

share of replicates whose reversed series gives the mirrored answer.

sequential

the reversal test fails in most replicates: the answer depends on the direction of time.

concat_k

median changepoints on c(x, x) (four expected, one for a single-change design).

dup_k

median changepoints when every observation is repeated twice, which makes the noise strongly autocorrelated: a measure of how badly the engine fails when its independence assumption does.

Source

data-raw/measure_engines.R (part invariance).

See also

Other measurement tables: cpt_calibration, cpt_data_types, cpt_noise_benchmark, cpt_null_sizes, cpt_runtimes

Examples

subset(cpt_invariances, !scale_invariant)$method
#> [1] "amoc"     "binseg"   "envcpt"   "fpop"     "geomcp"   "pelt"     "segneigh"
#> [8] "var"