Ten replicates of a 300-point series with mean changes of two marginal
standard deviations at 100 and 200, under independent Gaussian noise,
heavy-tailed noise (\(t_3\), scaled to unit variance), AR(1) noise with
\(\rho = 0.7\) and heteroscedastic noise (standard deviations 0.5, 1
and 2.5 by segment), for every univariate engine at its default and at
each setting of its noise-model argument. A recommendation is a call,
not a name: smuce and smuce with family = "hsmuce"
are different detectors.
Format
A tibble with one row per engine, setting and regime:
- method, setting
the engine and the argument setting (
"default"or the argument as written).- call
the
cpt_detect()call the row measures.- regime
"iid","heavy","ar1"or"hetero".- reps
replicates that ran.
- hits
mean number of the two real changes found within ten positions.
- fp
mean number of reported changepoints more than ten positions from either.
See also
Other measurement tables:
cpt_calibration,
cpt_data_types,
cpt_invariances,
cpt_null_sizes,
cpt_runtimes
Examples
ar <- subset(cpt_noise_benchmark, regime == "ar1")
ar[order(ar$fp), c("call", "hits", "fp")]
#> # A tibble: 42 × 3
#> call hits fp
#> <chr> <dbl> <dbl>
#> 1 "cpt_detect(x, method = \"fastcpd\", family = \"ar\")" 0.2 0
#> 2 "cpt_detect(x, method = \"wbsts\")" 0 0
#> 3 "cpt_detect(x, method = \"nsp\", variant = \"ar\")" 0 0.1
#> 4 "cpt_detect(x, method = \"pettitt\")" 0.8 0.2
#> 5 "cpt_detect(x, method = \"buishand\")" 0.7 0.3
#> 6 "cpt_detect(x, method = \"decafs\")" 0.5 0.3
#> 7 "cpt_detect(x, method = \"kcp\")" 1.9 0.3
#> 8 "cpt_detect(x, method = \"amoc\")" 0.6 0.4
#> 9 "cpt_detect(x, method = \"envcpt\")" 0.4 0.4
#> 10 "cpt_detect(x, method = \"snht\")" 0.6 0.4
#> # ℹ 32 more rows