Everyone, and welcome back to the course, we are now going to introduce the next section.
This section is about our first not naive forecasting method called Etes or Exponential Smoothing.
This lecture will give you an outline for this section and a brief description of what you will learn.
So the basic premise of this section is the moving average.
The moving average is a simple concept.
Imagine taking a window and sliding it across a time series.
And at each point in the Time series, you take the average of all the numbers in that window.
That's called the simple moving average.
So the simple moving average is equally weighted since each point you, including your average, matters
the same amount.
In contrast, we also have the exponentially weighted moving average, which weights each point exponentially
going back in time.
This kind of makes sense because it's saying Newar points matter more than older points.
This would make sense of your time.
Series's is dynamic and changing over time.
From this concept, we're going to build three different models that we can use to make forecasts.
The first model is called simple exponential smoothing, which can be used to forecast non trending,
non seasonal time series.
The second model is called the whole model, which can be used to forecast trending but non seasonal
time series.
The third model is called the whole Winters' Model, which can be used to forecast Time series with
both trends and seasonality.
So throughout this section, we'll look at how to apply these methods to various data sets as well as
some real life applications of these concepts.
Now, I want to note that the theory in this section is essentially optional.
There are basically two kinds of students who will take this course.
One kind will see the equations and realize immediately why they are useful.
It's because all these equations take on similar forms.
So when you see these forms, it's immediately obvious how they are being applied.
This should give you an intuitive understanding of these models and why they work.
The second kind of student will see equations and get kind of intimidated.
So for the second kind of student, I would recommend only watching the lecture.
It's for beginners and then skipping straight to the code.
You won't have an in-depth understanding of each model, but you'll know how to apply it in the real
world without having to be intimidated by math.
So in other words, only look at the math if it actually helps you.
If it doesn't, then feel free to settle for the plug and play approach.