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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.