Welcome back to Practical Time Series Analysis,
and welcome to week five.
We've been engaging the modeling process for an autoregressive or moving-average model.
We've been trying to determine the appropriate order of the model,
together with how to really estimate the coefficients
that have your model describe the data as well as we can.
What we're looking for this week is a measure of the quality, overall quality,
of the model, and we'll use the Akaike information criterion.
We'll look at some others as well.
The idea here is similar to what you might have seen in regression.
If you remember, in multivariate regression
you will have an adjusted r squared term very often,
that makes variables as they enter the model,
pay a little bit of a tax.
The idea is that we want to develop as simple as parsimonious model as possible.
So, we explore these ideas through the Akaike Information criterion.
We also try to increase the complexity of
our modeling process by looking at mixed models.
The ARMA models obviously,
the autoregressive moving-average, together with ARIMA,
integrated, autoregressive moving-average models,
that allow us to deal with a trend in our data.
We'll take at this point is a pretty powerful set of modeling ideas,
and begin to apply them to some real-world datasets that we find interesting.
Have a terrific week.