Simulates a GNAR process
Simulates a GNAR process with Normally distributed innovations.
GNARsim(n=200, net=GNAR::fiveNet, alphaParams=list(c(rep(0.2,5))), betaParams=list(c(0.5)), sigma=1, tvnets=NULL, netsstart=NULL)
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 |
sigma |
the standard deviation for the innovations. |
tvnets |
Only NULL is currently supported. |
netsstart |
Only NULL is currently supported. |
Parameter lists should not be NULL, set unused parameters to be zero. See GNARfit for model description.
GNARsim returns the multivariate time series as a ts object, with n rows and a column for each of the nodes in the network.
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.
#Simulate a GNAR(1,[1]) process with the fiveNet network GNARsim()
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.