| SNSeg {SNSeg} | R Documentation |
SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)
Description
The SNSeg package provides three functions for multiple change point
estimation using SN-based algorithms: SNSeg_Uni, SNSeg_Multi and SNSeg_HD.
Three critical value tables (critical_values_single,
critical_values_multi and critical_values_HD) were attached.
Functions MAR, MAR_Variance and MAR_MTS_Covariance can be utilized
to generate time series data that are used for the functions SNSeg_Uni, SNSeg_Multi and SNSeg_HD.
S3 methods plot(), print() and summary() are available for class "SNSeg_Uni",
"SNSeg_Multi" and "SNSeh_HD" objects. The function max_SNsweep enables users
to compute the SN test statistic and make the segmentation plot for these
statistics. The function SNSeh_estimate allows users to compute parameter
estimates of each segment that is separated by estimated change-points.
SNSeg_Uni
SNSeg_Uni provides SN-based change point estimates for a univariate
time series based on changes in a single parameter or multiple parameters.
For the parameters of the SN test, the function
SNSeg_Uni offers mean, variance, acf, bivariate
correlation and numeric quantiles as available options. It also allows users
to enter their own defined function as the input parameter. Besides, users can
use a composite set of parameters including one or more from the mean, variance,
acf or numeric quantiles quantile. To visualize the estimated change points,
users can set "plot_SN = TRUE" and "est_cp_loc = TRUE"
to generate the time series segmentation plot. The output comprises of the
parameter(s), the window size, and the estimated change point locations. The
function returns an S3 object of class "SNSeg_Uni", which can be applied to
S3 methods plot(), print() and summary().
SNSeg_Multi
SNSeg_Multi provides SN-based change point estimates for multivariate
time series based on changes in multivariate means or covariance matrix. The
"plot_SN = TRUE" option allows users to plot each individual time series and
the estimated change=points. The function returns an S3 object of class
"SNSeg_Multi", which can be applied to S3 methods plot(), print() and summary().
SNSeg_HD
SNSeg_HD provides SN-based change point estimates for a
high-dimensional time series based on changes in high-dimensional means. The
"plot_SN = TRUE" option allows users to plot each individual time series and
the estimated change=points. The input argument "n_plot" enables users to plot
the first "n_plot" number of time series. The function returns an S3 object of
class "SNSeg_HD", which can be applied to S3 methods plot(), print() and
summary().
max_SNsweep
max_SNsweep provides SN based test statistic of each time point and
generates a plot for these statistics and the estimated change-points.
SNSeg_estimate
SNSeg_estimate computes the parameter estimates of each segment separated
by the estimated change-points.
critical values table
The package SNSeg provides three critical values table.
Table critical_values_single tabulates critical values of SN-based
change point estimates based on the change in a single parameter.
Table critical_values_multi tabulates critical values of SN-based
change point estimates based on changes in multiple parameters.
Table critical_values_HD tabulates critical values of of SN-based
change point estimates based on changes in high-dimensional means.