Using sub-daily data

In this section, we will be using data from the Divvy bike share program in Chicago, Illinois. The data contains the number of bicycle rides taken each hour from the beginning of 2014 through the end of 2018 and exhibits a general increasing trend along with very strong yearly seasonality. Because it is hourly data and there are very few rides overnight (sometimes zero per hour), the data does show a density of measurements at the low end:

Figure 3.4 – Divvy number of rides per hour

Using sub-daily data such as this is much the same as using super-daily data, as we did with the Air Passengers data previously. You as the analyst need to use the freq argument and adjust the periods in the make_future_dataframe method, and Prophet will do the rest. If Prophet sees at least two days of data and the spacing between data is less than one day, it will fit a daily seasonality.

Let's see this by making a simple forecast. We already...