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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. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021)<doi:10.48550/arXiv.2112.13479>.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | LaplacesDemon, RSpectra |
| Published: | 2024-02-05 |
| 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_0.1.1.tar.gz |
| Windows binaries: | r-devel: OLCPM_0.1.1.zip, r-release: OLCPM_0.1.1.zip, r-oldrel: OLCPM_0.1.1.zip |
| macOS binaries: | r-release (arm64): OLCPM_0.1.1.tgz, r-oldrel (arm64): OLCPM_0.1.1.tgz, r-release (x86_64): OLCPM_0.1.1.tgz, r-oldrel (x86_64): OLCPM_0.1.1.tgz |
| Old sources: | OLCPM archive |
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