So in this video, we are going to summarize everything we learned in this section, this section was
about how to apply machine learning methods, two time series analysis.
Basically, the purpose of this section was not to discuss any particular machine learning method in
depth.
Instead, the main goal was to understand how to convert time series data into a format which is usable
by supervised machine learning methods, although we did discuss the intuition behind the models we
used.
I hope that your attention was on how to apply those models in the code.
What a nice side effect of using generic machine learning models is that we can do multi output multi-step
forecasts.
This makes it easier to predict multiple time steps ahead by making all the predictions at the same
time.
We learn that this can also improve forecast performance by avoiding error propagation that can occur
when you try to make predictions from other predictions.
We also learn that by using machine learning, it's just as easy to do classification as it is to do
a forecast.
With stock prices in particular, one common task is to simply do classification instead of trying to
predict an exact number.
Although this task is much simpler.
We found that it is still difficult to do much better than predicting the result of a coin toss.
In other words, this also lends more evidence to support the random walk hypothesis.
The main insight you should learn from this section is that it's very easy to build an auto regressive,
non-linear machine learning forecaster that makes it more powerful than the simple linear methods we
learned about before.
Now, whether or not that flexibility is useful is dependent on the data.
The data will decide which technique should be applied.
So use these techniques wisely and don't forget to benchmark.
If a linear model gives you the best error, then that is the best choice.
However, it's always worth trying these non-linear methods in case you can achieve superior results.