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. 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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | OLCPM_0.1.2.tar.gz |
303.1 KiB |
0.1.2 |
rolling linux/noble R-4.5 | OLCPM_0.1.2.tar.gz |
302.8 KiB |
0.1.2 |
rolling source/ R- | OLCPM_0.1.2.tar.gz |
201.0 KiB |
0.1.2 |
latest linux/jammy R-4.5 | OLCPM_0.1.2.tar.gz |
303.1 KiB |
0.1.2 |
latest linux/noble R-4.5 | OLCPM_0.1.2.tar.gz |
302.8 KiB |
0.1.2 |
latest source/ R- | OLCPM_0.1.2.tar.gz |
201.0 KiB |
0.1.2 |
2026-04-26 source/ R- | OLCPM_0.1.2.tar.gz |
201.0 KiB |
0.1.2 |
2026-04-23 source/ R- | OLCPM_0.1.2.tar.gz |
201.0 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | OLCPM_0.1.2.zip |
306.2 KiB |
0.1.2 |
2025-04-20 source/ R- | OLCPM_0.1.2.tar.gz |
201.0 KiB |