OK, so in this lecture, we will be discussing a surprising observation we have made in this section,
this observation was that Arnon's don't seem to perform particularly well when it comes to TIME series
analysis.
So before we even begin discussing the main point of this lecture, I have a question for you.
The students of this course, I know that many of you have assumed that ListBox must be excellent for
Time series.
My question is, why do you think this what gave you this idea?
So as an exercise for this lecture, if this was one of your assumptions coming into this course, please
let me know on the Q&A why you believe this to be true.
So your next question might be, why do Arnon's seem to perform so poorly?
Unfortunately, answers to questions like these are harder than you think.
A question like this might take up a whole Ph.D. thesis that is years of work.
My personal belief is that it's usually not worth your time to invest in the theory of deep neural networks
unless you have a very strong background in mathematics.
Furthermore, it's often the case that when people come up with theories about why things work or don't
work, they are likely to be superseded by new theories in the future.
So unless you want to devote years of your life studying a niche topic only for it to be obsolete a
few months later, don't do it unless it's truly what you love to do.
Now, my students know that I love inventing motto's about machine learning.
The relevant model for this situation is machine learning is experimentation and not philosophy.
What this means is that you often don't care about why something works or does not work.
You just care about the results.
The result is easy to check.
Simply do the experiment and look at what happens.
The nice thing about this is someone has already done these experiments.
There is a paper included in extra reading called Statistical and Machine Learning, Forecasting Methods,
Concerns and ways forward.
So in this paper, the authors compare both classical times, various methods like Hetson Arima, along
with machine learning and deep neural networks, including trees, support vector machines and LSD.
So let's look at the results.
So this is a chart showing estimate on the Y axis in computational complexity, on the X axis.
Basically, the higher you are, the worse error you have.
Well, which model is the highest on this chart?
That would be the Helstone.
Sadly, many of the machine learning methods perform poorly, showing that although these methods may
be powerful for other types of data, this is not the case for Time series forecasting.
Note that for this paper, the authors looked at the M3 competition, which is a data set with over
one thousand monthly time series.
So they certainly had a lot of data to work with to justify these results.