From my email today
You use an illustration of a seasonal arima model:
I would like to simulate data from this process then fit a model… but I am unable to find any information as to how this can be conducted… if I set phi1, Phi1, theta1, and Theta1 it would be reassuring that for large n the parameters returned by
Arima(foo,order=c(1,1,1),seasonal=c(1,1,1))are in agreement…
arima.sim() won’t handle seasonal ARIMA models. I wrote
simulate.Arima() to handle them, but it is designed to simulate from a fitted model rather than a specified model. However, you can use the following code to do it. It first “estimates” an ARIMA model with specified coefficients. Then simulates from it.
library(forecast) model <- Arima(ts(rnorm(100),freq=4), order=c(1,1,1), seasonal=c(1,1,1), fixed=c(phi=0.5, theta=-0.4, Phi=0.3, Theta=-0.2)) foo <- simulate(model, nsim=1000) fit <- Arima(foo, order=c(1,1,1), seasonal=c(1,1,1))