OLCPM: Online Change Point Detection for Matrix-Valued Time Series

We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. See more details in He et al. (2021)<arXiv:2112.13479>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: LaplacesDemon, RSpectra
Published: 2023-02-27
Author: Yong He [aut], Xinbing Kong [aut], Lorenzo Trapani [aut], Long Yu [aut, cre]
Maintainer: Long Yu <yulong at mail.shufe.edu.cn>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: OLCPM results


Reference manual: OLCPM.pdf


Package source: OLCPM_0.1.0.tar.gz
Windows binaries: r-devel: OLCPM_0.1.0.zip, r-release: OLCPM_0.1.0.zip, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available


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