Self-Normalization(SN) Based Change-Point Estimation for Time Series
A funtion to generate a multivariate autoregressive process (MAR) in t...
A Funtion to generate a multivariate autoregressive process (MAR) mode...
A funtion to generate a multivariate autoregressive process (MAR) mode...
SN-based test statistic segmentation plot for univariate, mulitivariat...
Plotting the output for high-dimensional time series with dimension gr...
Plotting the output for multivariate time series with dimension no gre...
Plotting the output for univariate or bivariate time series (testing t...
Print SN-based change-point estimates for high-dimensional time series...
Print SN-based change-point estimates for multivariate time series wit...
Print SN-based change-point estimates for univariate or bivariate time...
SNSeg: An R Package for Time Series Segmentation via Self-Normalizatio...
Parameter estimates of each segment separated by Self-Normalization (S...
Self-normalization (SN) based change points estimation for high dimens...
Self-normalization (SN) based change points estimation for multivariat...
Self-normalization (SN) based change point estimates for univariate ti...
Summary of SN-based change-point estimates for high-dimensional time s...
Summary of SN-based change-point estimates for multivariate time serie...
Summary of SN-based change-point estimates for univariate or bivariate...
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) <doi:10.1111/rssb.12552>.