GNARsim: Simulates a GNAR process

In GNAR: Methods for Fitting Network Time Series Models

Simulates a GNAR process

Description

Simulates a GNAR process with Normally distributed innovations.

Usage

GNARsim(n=200, net=GNAR::fiveNet, alphaParams=list(c(rep(0.2,5))),
 betaParams=list(c(0.5)), sigma=1, tvnets=NULL, netsstart=NULL)

Arguments

n	time length of simulation.
net	network used for the GNAR simulation.
alphaParams	a list containing vectors of auto-regression parameters for each time-lag.
betaParams	a list of equal length as alphaParams containing the network-regression parameters for each time-lag.
sigma	the standard deviation for the innovations.
tvnets	Only NULL is currently supported.
netsstart	Only NULL is currently supported.

Details

Parameter lists should not be NULL, set unused parameters to be zero. See GNARfit for model description.

Value

GNARsim returns the multivariate time series as a ts object, with n rows and a column for each of the nodes in the network.

References

Knight, M.I., Nunes, M.A. and Nason, G.P. Modelling, detrending and decorrelation of network time series. arXiv preprint.

Knight, M.I., Leeming, K., Nason, G.P. and Nunes, M. A. (2020) Generalised Network Autoregressive Processes and the GNAR package. Journal of Statistical Software, 96 (5), 1–36.

Examples

#Simulate a GNAR(1,[1]) process with the fiveNet network
GNARsim()

GNAR documentation built on April 28, 2023, 1:12 a.m.