Automatic trend changepoint detection

Trend changepoints are locations in your time series where the trend component of the model suddenly changes its slope. There are many reasons why these changepoints occur, depending upon your dataset. For example, Facebook (now Meta) developed Prophet to forecast its own business problems; it may model the number of daily active users and see a sudden change of trend upon the release of a new feature.

Airline passenger numbers may suddenly change as economies of scale allow much cheaper flights. The trend of carbon dioxide in the atmosphere was relatively flat for tens of thousands of years, but then suddenly changed during the Industrial Revolution.

From our work with the Divvy dataset in previous chapters, we saw a slowdown of growth after approximately two years. Let’s take a closer look at this example to learn about automatic changepoint detection.

Default changepoint detection

Prophet sets changepoints by first specifying...