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SNSeg (version 1.0.2)

Self-Normalization(SN) Based Change-Point Estimation for Time Series

Description

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) .

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Version

Install

install.packages('SNSeg')

Monthly Downloads

567

Version

1.0.2

License

GPL (>= 3)

Maintainer

Zifeng Zhao

Last Published

March 8th, 2024

Functions in SNSeg (1.0.2)

Plotting the output for high-dimensional time series with dimension greater than 10

critical_values_HD

Critical Values of Self-Normalization (SN) based test statistic for changes in high-dimensional means (SNHD)

SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)

plot.SNSeg_Multi

Plotting the output for multivariate time series with dimension no greater than 10

summary.SNSeg_Uni

Summary of SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)

Self-normalization (SN) based change points estimation for high dimensional time series for changes in high-dimensional means (SNHD).

critical_values_multi

Critical Values of Self-Normalization (SN) based test statistic for changes in multiple parameters (SNCP)

Plotting the output for univariate or bivariate time series (testing the change in correlation between bivariate time series)

Print SN-based change-point estimates for high-dimensional time series with dimension greater than 10

summary.SNSeg_HD

Summary of SN-based change-point estimates for high-dimensional time series with dimension greater than 10

summary.SNSeg_Multi

Summary of SN-based change-point estimates for multivariate time series with dimension no greater than 10

Self-normalization (SN) based change point estimates for univariate time series

Self-normalization (SN) based change points estimation for multivariate time series

print.SNSeg_Multi

Print SN-based change-point estimates for multivariate time series with dimension no greater than 10

print.SNSeg_Uni

Print SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)

Parameter estimates of each segment separated by Self-Normalization (SN) based change-point estimates

critical_values_single

Critical Values of Self-Normalization (SN) based test statistic for the change in a single parameter (SNCP)

SN-based test statistic segmentation plot for univariate, mulitivariate and high-dimensional time series

A funtion to generate a multivariate autoregressive process (MAR) model in time series for testing change points based on variance and autocovariance

A funtion to generate a multivariate autoregressive process (MAR) in time series

MAR_MTS_Covariance

A Funtion to generate a multivariate autoregressive process (MAR) model in time series. It is used for testing change-points based on the change in multivariate means or multivariate covariance for multivariate time series. It also works for the change in correlations between two univariate time series.