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>.

Getting started

Package details

AuthorYong He [aut], Xinbing Kong [aut], Lorenzo Trapani [aut], Long Yu [aut, cre]
MaintainerLong Yu <fduyulong@163.com>
LicenseGPL-2 | GPL-3
Package repositoryView on CRAN
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OLCPM documentation built on June 22, 2024, 9:26 a.m.