Summary

In this chapter, you learned how to use Prophet's performance metrics to extend the usefulness of cross-validation. You learned about the six metrics Prophet has out of the box, namely mean squared error, root mean squared error, mean absolute error, mean absolute percent error, median absolute percent error, and coverage. You learned many of the advantages and disadvantages of these metrics, and situations where you may want to use or avoid any one of them.

Next, you learned how to create Prophet's performance metrics DataFrame and use it to create a plot of your preferred cross-validation metric so as to be able to evaluate the performance of your model on unseen data across a range of forecast horizons. You then used this plot with the World Food Programme's rainfall data to see a situation where Prophet's automatic cut-off date selection is not ideal, and how to create custom cut-off dates.

Finally, you brought all of this together in an exhaustive...