Simulating from a specified seasonal ARIMA model

January 26, 2017
By

(This article was first published on R – Hyndsight, and kindly contributed to R-bloggers)

From my email today

You use an illustration of a seasonal arima model:

ARIMA(1,1,1)(1,1,1)4

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…

My answer:

Unfortunately 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))

To leave a comment for the author, please follow the link and comment on their blog: R – Hyndsight.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)