Using data with regular gaps
Throughout your career, you may encounter datasets with regular gaps in reporting, particularly when the data was collected by humans who have working hours, personal hours, and sleeping hours. It simply may not be possible to collect measurements with perfect periodicity.
As you will see when we look at outliers in a later chapter, Prophet is robust in handling missing values. However, when that missing data occurs at regular intervals, Prophet will have no training data at all during those gaps to make estimations with. The seasonality will be constrained during periods where data exists but unconstrained during the gaps, and Prophet’s predictions can exhibit much larger fluctuations than the actual data displays. Let’s see this in action.
Suppose that Divvy’s data had only been collected between the hours of 8 a.m. and 6 p.m. each day. We can simulate this by removing the data from outside these hours from our DataFrame:
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