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OLCPM
View on CRAN: Click
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Download and install OLCPM package within the R console
Install from CRAN:
install.packages("OLCPM")
Install from Github:
library("remotes")
install_github("cran/OLCPM")
Install by package version:
library("remotes")
install_version("OLCPM", "0.1.2")
Attach the package and use:
library("OLCPM")
Maintained by
Long Yu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-27
Latest Update: 2024-02-05
Description:
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).
How to cite:
Long Yu (2023). OLCPM: Online Change Point Detection for Matrix-Valued Time Series. R package version 0.1.2, https://cran.r-project.org/web/packages/OLCPM. Accessed 23 Nov. 2024.
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