tensorTS: Factor and Autoregressive Models for Tensor Time Series

Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <doi:10.1080/01621459.2021.1912757>, Chen et al (2020) <doi:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.

Version: 1.0.1
Imports: tensor, rTensor, expm, methods, stats, MASS, abind, Matrix, pracma, graphics
Published: 2023-05-07
Author: Zebang Li [aut, cre], Ruofan Yu [aut], Rong Chen [aut], Yuefeng Han [aut], Han Xiao [aut], Dan Yang [aut]
Maintainer: Zebang Li <zl326 at stat.rutgers.edu>
BugReports: https://github.com/ZeBang/tensorTS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/zebang/tensorTS
NeedsCompilation: no
Materials: README
In views: TimeSeries
CRAN checks: tensorTS results

Documentation:

Reference manual: tensorTS.pdf

Downloads:

Package source: tensorTS_1.0.1.tar.gz
Windows binaries: r-devel: tensorTS_1.0.1.zip, r-release: tensorTS_1.0.1.zip, r-oldrel: tensorTS_1.0.1.zip
macOS binaries: r-release (arm64): tensorTS_1.0.1.tgz, r-oldrel (arm64): tensorTS_1.0.1.tgz, r-release (x86_64): tensorTS_1.0.1.tgz
Old sources: tensorTS archive

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