So in this lecture, we are going to summarize everything we learned in this section.
This section looked at times there is basics which was designed to prepare you for the rest of the course.
We first started with a very simple question.
What is a time series?
Hopefully you now have a better understanding of what a time series is and what constraints must be
met so that they apply to the methods we will study.
We next looked at the concept of modeling versus prediction.
This is an important distinction because sometimes we'll be focused on one and other times will be focused
on the other.
You should now understand why modeling is useful and in fact can aid and prediction, especially when
it gives you hints about when prediction is a waste of time.
The next step was to consider the shape of our data sets in this course, since we'll be working with
Python and Python libraries.
There are certain conventions that we normally adhere to.
You should also now understand why having this reflex of automatically thinking about shapes will help
your intuition in machine learning.
The next step was to discuss different time series tasks.
We looked at the one step forecast, the multi-step forecast and the multi output multi-step forecast.
We also considered the practical applications of time series classification, which we will study in
this course.
The next step was to learn about common data transformations used in Time series, the transformations
we studied all have a common property, which is that they tend to squash large values.
We learned about why this is a deeply fundamental operation reflected in nature itself.
We next learned about Time series metrics, which are more numerous compared to other fields of data
analysis and machine learning.
This also taught us about one of the major themes of this course, which is that there are so many options
to choose from.
Exercises for this course are essentially built in because there are many combinations.
We simply do not have time to try.
Your job in this course will be to try different combinations you think might work and see if you can
obtain a superior result.
The next step for us was to turn our attention to Financial Times series, we learned about important
concepts such as the log price and the log return.
We then had a chance to do some practical work which involved simulating a stock price time series.
In the next lecture, we learned about why that practical work was useful when we studied the random
walk hypothesis in depth.
We then learned about the naive forecasts and why it's extremely important to have baseline predictions
for random walks in particular.
It turns out that the naive forecast is the best forecast.
The final exercise for this section was to implement the new forecasts and to test out different metrics.
So hopefully you learned a lot and we'll see you in the next lecture.