Summary

In this chapter, you learned how to control the fit of the trend line by using changepoints. First, you used Divvy data to see how Prophet automatically selects potential changepoint locations and how you can control this by modifying the default number of potential changepoints and the changepoint range.

Then, you learned a more robust way to control Prophet’s changepoint selection through regularization. Just as with seasonality and holidays, changepoints are regularized by setting the prior scale. You then looked at the Instagram data of James Rodríguez and learned how to model the increase in likes per post he received both during and after the World Cups of 2014 and 2018. Finally, you learned how to blend these two techniques and enrich an automatically selected grid of potential changepoints with your custom changepoint locations.

In the next chapter, we will again look at the Divvy data, but this time, we’ll include the additional columns for...