Everyone, and welcome back to TIME series analysis.
So this is the next section of the course and we are now going to focus on Arima in this lecture.
We'll introduce this section and we'll give you an outline of what you'll learn.
So the basic strategy we are going to follow in this section is this we'll start by learning the basics
of the model, the model and the full Arima model is designed this way to get you coding as quickly
as possible.
Once you understand each kind of model, you'll be able to apply it to real data right away.
Now, of course, that's not the end of the story.
The remainder of this section will focus on the details and applications, so using a remote effectively
is all about choosing the right Arima orders.
Basically, you need to specify how many parts data points your model should depend on.
The classic way of doing this is to use the ATF and the ATF.
These are special plots that you can look at to directly choose your model orders.
Now, one thing I really dislike about most times the resources is that they only tell you the rules
for how to use the ATF and PKF.
But they miss the most important part, in my opinion.
And that is proving to you that these rules really work and that there is real logic behind why we should
use them.
So in this course, we're going to do an exercise that I've never seen in any time series course, which
is to demonstrate that the rules for how to use the ATF and the ATF really work.
This will involve running some simulations, just like how you do when you want to simulate stock prices.
Doing this exercise will give you a better understanding of how to use the AKF and the passive compared
to if you would just memorize rules for no reason.
So in modern times, while using the Akef in the pickoff are useful, we can also make use of computational
power in order to do model selection.
We'll learn how to use auto arima and we'll see how that stacks up against more classical methods like
the Akef in the.
Now, Ottowa Rimma exposes us to a few more extensions of the basic Arima model specifically, we can
also add seasonality as well as exoticness data.
At this point, it's worth trying to understand how auto arima actually works.
How does it select the best model?
What criteria does it use to define what is best?
So we'll discuss this as well.
In statistics and specifically regression analysis, it's common to use the AC in the back.
For some reason, statisticians really like to have lots of options all at once, even though they can
only pick one.
So we'll just have to learn about both.
Once we've completed our study of how a remote works, well then apply it to even more data sets.
We'll look at both sales data and stock prices.
Now, stock prices are an interesting one because Arima gives us the tools to really understand what's
going on from an objective and statistical point of view.
So there are several important things we'll do in the section regarding stock prices.
First will apply Otto Arima in order to see what the best model will be, at least according to the
Otto Arima package.
We'll also compare those Arima predictions with the new forecast.
Remember that this is one of the most important things to do when evaluating your models will then apply.
What we learned about the AKF and the pickoff to stock returns, that is, will apply the classical
method to see what they have to tell us about the nature of stock prices.
This will bring us back to the random walk hypothesis.
We'll learn what our new analysis methods have to say.
Another important topic in this session is learning about how a rhema makes its forecasts, this kind
of detail is hidden from you because when you use a library, all you're doing is calling the function
to get forecast.
It doesn't tell you anything about how those forecasts are really made.
If you've been taking my courses for long enough, you know that I often speak out against bloggers
and marketers and Udemy instructors who make content about predicting stock prices with storms.
This is mostly because they do so incorrectly.
And the mistake that they make is that they don't understand how to forecast their plots cheat most
of the time because they use true values from the future, when in reality those values would be unavailable.
And if you don't understand why this is bad.
Well, here are two simple reasons.
Number one, if you take any code like that seriously and you try to use it in the real world, you
will lose money.
Number two, you'll be wasting time learning things which are incorrect and even worse, including those
projects in your portfolio, which will make you look very bad in front of potential employers.
So this lecture should help you do your due diligence to determine what is a legitimate forecast and
what is not.