Welcome back to Practical Time Series Analysis,
and welcome to week two.
Now that we have many of our mathematical and statistical preliminaries out of the way,
we can start engaging actual time series themselves.
So, what we'll do this week is learn how to take advantage of
the tremendous insights and intuition we can get
just by doing a simple XY plot for a time series,
looking for trends, seasonality, and other patterns.
We'll begin learning how to analyze time series with
the many functions available to us through R. In particular,
we'll start looking at the Autocorrelation Function.
Now, if you just have a sequence of unrelated data points,
in essence, if you just have noise,
well, you're not going to see much structure there.
But very often, times series do exhibit
a relationship between neighbors that we can describe with the Autocorrelation Function.
And this week, we'll learn how to obtain those and how to start using those.
We can also get a lot of insight into the processes that generate time series,
your so-called Stochastic processes.
If we can write some software simulations ourselves,
and we begin in week two,
writing very simple but nonetheless useful,
simulated data, a simulated time series.
So, welcome to week two.