Package: tensorTS 1.0.2

Zebang Li

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>.

Authors:Zebang Li [aut, cre], Ruofan Yu [aut], Rong Chen [aut], Yuefeng Han [aut], Han Xiao [aut], Dan Yang [aut]

# Install 'tensorTS' in R:

install.packages('tensorTS', repos = c('https://zebang.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated 30 days agofrom:ccb5e8b07b. Checks:9 OK. Indexed: yes.

Help page	Topics
Estimation for Reduced Rank MAR(1) Model	matAR.RR.est
Asymptotic Covariance Matrix of One-Term Reduced rank MAR(1) Model	matAR.RR.se
Plot Matrix-Valued Time Series	mplot
Plot ACF of Matrix-Valued Time Series	mplot.acf
Predict funcions for Tensor Autoregressive Models	predict.tenAR
Simulate taxi data by tenAR models	taxi.sim.AR
Simulate taxi data by factor models	taxi.sim.FM
Estimation for Autoregressive Model of Tensor-Valued Time Series	tenAR.est
Generate TenAR(p) tensor time series	tenAR.sim
Estimation for Tucker structure Factor Models of Tensor-Valued Time Series	tenFM.est
Rank Determination for Tensor Factor Models with Tucker Structure	tenFM.rank
Generate Tensor Time series using given Factor Process and Factor Loading Matrices	tenFM.sim