Consider a (stationary) autoregressive process, say of order 2,
for some white noise with variance . Here is a code to generate such a process,
> phi1=.5
> phi2=-.4
> sigma=1.5
> set.seed(1)
> n=240
> WN=rnorm(n,sd=sigma)
> ...

Check them out.
Here are thirty homoskedastic ones:
> homo.wiener for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener for (j in 1:30) {
plot( homo.wiener, type = "l", col = rgb(.1,....

Check them out.
Here are thirty homoskedastic ones:
> homo.wiener for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener for (j in 1:30) {
plot( homo.wiener, type = "l", col = rgb(.1,....