Here you will find daily news and tutorials about R, contributed by over 750 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

This post join two things that I like: Highcharts and the Time series. So I have a lot of fun making this minipost
to show how works an autoregressive process. Well, let’s remember the structure of an autoregresive process.
$y_t = varphi,y_{t-1}+epsilon_t$, where $epsilon_t$ is a white noise, i.e, a $cov(epsilon_j, epsilon_i) = 0$
if $i neq j$, $Var(epsilon_t) = sigma^2_epsilon$, and $varphi$ is a real parameter.

Now, let’s see first how a white noise looks like. The next chart show the process $y_t = epsilon_t$, so if we see
for a moment the chart we’ll notice the process look likes an independent random sequence. There’s no structure
of any dependence.

The two charts above show autoregressive process for two distinct values of $varphi$. It’s easy to see the difference.
In the first process the next value of the serie tends to have the same sign of the past value. In the other procces
ocurrs the oposite. That’s the effect of the sign of the parameter.

It’s interesting to note: the two process are diferents but they have the same mean and variance. In fact,
in the $AR(1)$ process $Var(y_t) = frac{sigma_epsilon^2}{1-varphi^2}$, only if the process are stationary $|varphi| < 1$.

Related

To leave a comment for the author, please follow the link and comment on their blog: Jkunst - R category.