Using monthly data

In Chapter 2, Getting Started with Facebook Prophet, we built our first Prophet model using the Mauna Loa dataset. The data was reported every day, which is what Prophet by default will expect and is therefore why we did not need to change any of Prophet's default parameters. In this next example, though, let's take a look at a new set of data that is not reported every day, the Air Passengers dataset, to see how Prophet handles this difference in data granularity.

This is a classic time series dataset spanning 1949 through 1960. It counts the number of passengers on commercial airlines each month during that period of explosive growth in the industry. The Air Passengers dataset, in contrast to the Mauna Loa dataset, has one observation per month. What happens if we attempt to predict future dates?

Let's create a model and plot the forecast to see what happens. We begin as we did with the Mauna Loa example, by importing the necessary libraries...