Gaussian white noise R code

The above graph denotes a gaussian white noise that was generated using 100 random numbers in R.

rnorm : Used to generate a random normal distribution.

ACF : Autocorrelation function display the cross-correlation of a signal with itself. In the above graph you can see none of the lags of the signal are dependent on the signal itself. That supports the theory that this is a gaussian white noise.

Also, notice in the time series graph above, the signal looks mean reverting and the volatility appears to be constant. That proves that the signal is a covariance stationary process.

Below is the code that generate the graph above:

	set.seed(12345)
	gwn <- rnorm(100)
	layout(matrix(1:4, ncol = 2, nrow = 2))
	plot.ts(gwn, xlab = "", ylab = "")
	abline(h = 0, col = "red")
	acf(gwn, main = "ACF")
	qqnorm(gwn)
	pacf(gwn, main = "PACF")

view raw gwn.r hosted with ❤ by GitHub