As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.
The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).
That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).
I'm working w/ like-minded people from reddit/HN to build intentional communities (commune, essentially).
Start w/ small community, build a glamping sustainable living space...
Tent living but not complete roughing it, communal shower, restrooms, kitchen, gardens, laundry, and worker spaces...then little living areas will be setup by families with gates so children don't run off, or get lost.
Ideally in warmer climes as tents/yurts I think would be hard to keep warm in winter than cool in summer...
The idea being that the commune could pool resources and grow/expand and work towards fixing earth by making it fashionable to live in such communities...
I've actually been "playing"/"reading" a lot of them from https://www.choiceofgames.com/.
They also offer a scripting language called "ChoiceScript" that allow anyone to make their own games.
I'm sure it's not super popular, but they have their niche audience.
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The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).
That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).