Summary

In this chapter, you took the lessons learned from the basic Mauna Loa model you built in Chapter 2, Getting Started with Facebook Prophet, and learned what changes you need to make when the period of your data is not daily. Specifically, you used the Air Passengers dataset to model monthly data and used the freq argument when making your future DataFrame in order to hold back Prophet from predicting daily.

Then, you used the hourly data from Divvy's bike share program to set the future frequency to hourly so that Prophet would increase the granularity of its prediction timescale. Finally, you simulated periodic missing data in the Divvy dataset and learned a different way to match the future DataFrame's schedule to that of the training data, in order to prevent Prophet from making unconstrained predictions.

Now that you know how to handle the different datasets you will encounter in this book, you're ready for the next topic! In the next chapter, you...