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tensorTS

Factor and Autoregressive Models for Tensor Time Series

The R package tensorTS includes methods in our recent papers, including Factor and Autoregressive Models for High-Dimensional tensor Time Series. To have more details please see the manual file for the full documentation.

Installation

You can install the released version of tensorTS from CRAN with:

install.packages("tensorTS")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ZeBang/tensorTS")

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Install

install.packages('tensorTS')

Monthly Downloads

645

Version

1.0.1

License

GPL (>= 2)

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Maintainer

Zebang Li

Last Published

May 7th, 2023

Functions in tensorTS (1.0.1)

tenFM.rank

Rank Determination for Tensor Factor Models with Tucker Structure
tenFM.sim

Generate Tensor Time series using given Factor Process and Factor Loading Matrices
matAR.RR.est

Estimation for Reduced Rank MAR(1) Model
tenAR.sim

Generate TenAR(p) tensor time series
mplot

Plot Matrix-Valued Time Series
taxi.sim.AR

Simulate taxi data by tenAR models
tenFM.est

Estimation for Tucker structure Factor Models of Tensor-Valued Time Series
predict.tenAR

Predict funcions for Tensor Autoregressive Models
mplot.acf

Plot ACF of Matrix-Valued Time Series
matAR.RR.se

Asymptotic Covariance Matrix of One-Term Reduced rank MAR(1) Model
tenAR.est

Estimation for Autoregressive Model of Tensor-Valued Time Series
taxi.sim.FM

Simulate taxi data by factor models