So in this lecture, we will be starting the next section of this course, which is on Facebook's private
library.
I really like this package for various reasons.
The first reason is that you'll see it's a package built with business data sets in mind.
So really, anyone who runs a business or works for a business as a data scientist can derive value
from Facebook profit.
For example, you can track and forecast sales.
You can track and forecast CPU usage.
You can track and forecast your advertising spend and so forth.
If you are running a business, then likely you have something to count.
If that count varies over time, then you have a time series and you can apply these methods in addition,
because it's meant for business data.
It's very easy to take into account different factors that businesses deal with, like weekends, holidays
and even special events like the Super Bowl.
It's also very easy to add your own custom exogenous inputs.
For example, you might add a special input to the NFL Sundays, which you're not every Sunday, but
only for specific portions of the year.
So if you're a consultant or a freelancer and your client needs a time series, forecasting solution,
profit can be an excellent choice.
OK, so the number two reason I like this package, it's very easy to use with just a few lines of code
and it's relatively fast, you'll find that models like Arima can struggle.
When your time series is long with profit, you'll find that most things can be accomplished with just
a single function call even cross-validation, which, as we've seen, can take some work when you write
it yourself.
The number three reason I like this package is because it's very interpretable.
The model used by profit is most reminiscent of exponential smoothing.
If you want to compare it to the other models we've studied, essentially your model can be broken down
into error, trend and seasonality after fitting a model profit gives you the tools to automatically
plot these components again with just a single line of code.
But unlike its profit, can actually model seasonality at multiple frequencies.
So you might have a pattern at the weekly level and you might have a pattern at the yearly level.
For example, sales might slow down on Sundays, but sales might increase on Black Friday.
So one is an example of a weekly recurring pattern and the other is an example of a yearly recurring
pattern.
OK, so I hope you're convinced that Facebook profit is a valuable addition to your time series toolkit,
if you're a consultant or a freelancer, your clients will truly appreciate such a simple, accurate
and interpretable solution.
If you run your own business, then I'm sure you'll find that these features are useful for yourself
as well.