So in this lecture, we will be summarizing what we learned in this section, which was all about a
Facebook time series library called Profit, we observe that this library is a lot like the ETS methods
we learned about before and that they both explicitly model trend and seasonality.
But profit has many differences compared to the methods we've studied before.
Firstly, we saw that profit is not auto regressive.
Instead, it's only regressive.
It is time itself.
Because of this, we saw that profit handles missing data with ease.
Furthermore, we saw that profit allows you to explicitly handle holidays.
You can either enter these yourself or you can use profits built in functions for standard holidays
in each country.
We also saw that profit model seasonality at multiple scales at once.
Unlike other methods, it's able to model yearly, weekly and daily seasonal patterns using a four year
series.
Finally, we learned that the profit model is a Bayesian model.
Thus, when you're working with clients, you can help them tune the model by modifying its priors.
Because of these factors, we learn that profit is an excellent choice for business use cases such as
modeling, sales, CPU usage or ads.
But we also learn that it is not very good at predicting stock prices, as some blog articles would
have you believe.
In this section, we learned how easy it is to use.
Let's go over the steps one more time to summarize what we've learned.
So we always start by instantiating a profit object.
At this point, we can choose various options like the growth pattern, the types of seasonality and
the Pryors.
The next step is to fit the model.
As you saw, this requires a specially formatted data frame with the specific columns D and Y, optionally,
you can add more columns as exoticness or grassers.
However, you have to be aware that when you make a forecast, you must have the values of those exoticness
progressors for the forecasting dates.
The next step is to make predictions, which actually involves multiple steps.
We start by creating a placeholder data frame, which we call future.
This contains the dates we want to make predictions for when we call, predict profit, takes these
dates and makes predictions for the given dates.
Once we have our predictions, we can easily plot them with a single call to the plot function.
Those plots, both the sample predictions and the out of sample forecast.
And with just one additional function call, we can plot the change points as well.
The next step is to applaud the individual components of our prediction, which, again, is just a
single call.
So this gives us the trend that seasonal and holiday components and external progressors if you had
them.
We also learned that we can do cross-validation and just a single line of code which works precisely
like the walk forward validation I previously described.
This returns a data frame of predictions from which we can compute performance metrics.