The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Nonparametric change point detection for multivariate time series. Implements the NP-MOJO methodology proposed in
McGonigle, E. T., Cho, H. (2023). Nonparametric data segmentation in multivariate time series via joint characteristic functions. arXiv preprint arXiv:2305.07581.
You can install the released version of CptNonPar from CRAN with:
install.packages("CptNonPar")You can install the development version of CptNonPar from GitHub with:
devtools::install_github("https://github.com/EuanMcGonigle/CptNonPar")For further examples, see the help files within the package. We can generate an example for change point detection as follows.
We generate a univariate time series of length 1000, with a mean change at time 300, and an autocovariance (but not marginal) change at time 650. Then, we perform the multi-lag NP-MOJO algorithm with lags 0 and 1, and print the estimated change points and the associated clusters:
library(CptNonPar)
n <- 1000
set.seed(123)
noise1 <- stats::arima.sim(model = list(ar = -0.5), n = n, sd = sqrt(1 - 0.5^2))
noise2 <- stats::arima.sim(model = list(ar = 0.5), n = n, sd = sqrt(1 - 0.5^2))
noise <- c(noise1[1:650], noise2[651:n])
signal <- c(rep(0, 300), rep(0.7, 700))
x <- signal + noise
x.c <- np.mojo.multilag(x, G = 166, lags = c(0, 1))
x.c$cpts
#> cp lag p.val
#> [1,] 295 0 0.000
#> [2,] 648 1 0.005
x.c$cpt.clusters
#> [[1]]
#> cp lag p.val
#> [1,] 295 0 0
#> [2,] 296 1 0
#>
#> [[2]]
#> cp lag p.val
#> [1,] 648 1 0.005These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.