tenAR.sim: Generate TenAR(p) tensor time series

In ZeBang/timeFA: Factor and Autoregressive Models for Tensor Time Series

Generate TenAR(p) tensor time series

Description

Simulate from the TenAR(p) model.

Usage

tenAR.sim(t, dim, R, P, rho, cov, A = NULL, Sig = NULL)

Arguments

t	length of output series, a strictly positive integer.
dim	dimension of the tensor at each time.
R	Kronecker rank for each lag.
P	autoregressive order.
rho	spectral radius of coefficient matrix \Phi.
cov	covariance matrix of the error term: diagonal ("iid"), separable ("mle"), random ("svd").
A	coefficient matrices. If not provided, they are randomly generated according to given dim, R, P and rho. It is a multi-layer list, the first layer for the lag 1 \le i \le P, the second the term 1 \le r \le R, and the third the mode 1 \le k \le K. See "Details" of tenAR.est.
Sig	only if cov=mle, a list of initial values of \Sigma_1,\ldots,\Sigma_K. The default are identity matrices.

Value

A tensor-valued time series generated by the TenAR(p) model.

See Also

tenFM.sim

Examples

set.seed(123)
dim <- c(3,3,3)
xx <- tenAR.sim(t=500, dim, R=2, P=1, rho=0.5, cov='iid')

ZeBang/timeFA documentation built on April 23, 2024, 7:33 a.m.