Package: OLCPM
Type: Package
Title: Online Change Point Detection for Matrix-Valued Time Series
Version: 0.1.2
Authors@R: c(person("Yong", "He", role=c("aut")),
       person("Xinbing", "Kong", role=c("aut")),
       person("Lorenzo", "Trapani", role=c("aut")),
       person("Long", "Yu", role=c("aut", "cre"), email = "fduyulong@163.com"))
Author: Yong He [aut],
  Xinbing Kong [aut],
  Lorenzo Trapani [aut],
  Long Yu [aut, cre]
Maintainer: Long Yu <fduyulong@163.com>
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)<doi:10.48550/arXiv.2112.13479>.
License: GPL-2 | GPL-3
Encoding: UTF-8
Imports: LaplacesDemon, RSpectra
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2024-05-29 01:45:53 UTC; hey12
Repository: CRAN
Date/Publication: 2024-05-30 07:10:17 UTC
RoxygenNote: 7.3.1
LazyData: true
Built: R 4.3.3; ; 2025-01-24 10:34:38 UTC; unix
