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 |
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