Hey, everyone, and welcome back to the course.
And we are now going to introduce the next section.
So this section is all about time series basics and a primer on finance.
Since I know many of the students in this course are interested in time series analysis specifically
for financial applications.
So let's briefly outline what we will discuss in this section and why.
So we'll start this section by answering a very basic question.
What is a Time series?
Now, although you may have some preexisting intuition about what a Time series is, this will give
us a concrete definition to work with.
In this class, we'll also provide many examples to get you thinking about applications that you may
not have considered before.
Next, we'll discuss the concepts of modeling and predicting.
Now, most new students to machine learning think of these as the same thing, but in fact, they are
separate.
There are times where we care more about one over the other.
It's important to be able to identify each of these tasks and to know when you are doing what.
So the next step is to answer another basic question Why do we care about shapes?
This is a concept that is not covered in most courses in books, but that I feel is very important.
So you'll see this concept a lot in my courses, especially courses about images and sequences.
When you're able to visualize your data automatically because you inherently understand its shape,
this is a powerful reflex.
The next step is to discuss the kinds of time series tasks.
Now, most people think of time series analysis is just one thing, but in fact there are multiple approaches.
We have one set of forecasts, multi step forecasts and multi output forecasts.
We also have time series classification, which is typically not discussed in a traditional time series
course.
The next step is to discuss data transformations specifically of the box Cox variety.
Time series transformations are a bit different from regular machine learning, so you'll want to understand
the common transformations and why we use them.
The next step is to discuss forecasting metrics.
Now, for some reason, Time series analysis has a lot of different metrics.
My personal preference is to just stick to one, but in order to understand papers and communicate with
your colleagues, these are important to know.
After this, we'll do a Financial Time series Primer.
This will introduce you to important quantities specific to finance, such as the net return gross return
log return log prices, adjusted, close dividends and so forth.
So if you want to do finance, although Financial Time series do look like regular time series, you'll
still want to know what these things mean.
Next, we'll have a chance to write a bit of code when we do stock price simulations.
Now, this might seem a little random, but the idea is to get you started early with doing practical
work so that we can build on these skills later in the course.
Speaking of which, the next lecture will build on these price simulations.
At this point, we'll bring things back to the theory world and gain more insight about the meaning
behind those price simulations.
In fact, those price simulations were not that far from reality.
We'll discuss the random walk and the corresponding random walk hypothesis.
This is a very important concept in time series, analysis and finance because it tells you what is
possible and what is impossible to forecast.
In the next lecture, we'll discuss the naive forecast, as well as the importance of having a baseline
prediction.
In practice, the naive forecast often serves as a baseline model with which you can compare other models.
It might seem like a weak tool when you compare it alongside powerful machine learning models, but
you'll be surprised that the results we observe.
The next step in this section will be to do a little more practical work where we learn about how to
implement and evaluate the naive forecast and code.
This will teach us how to actually perform the naive forecast and make use of the metrics we learned
about earlier.
So that's the outline for this section.
Thanks for listening and I'll see you in the next lecture.