Most time series models assume you've already deseasonalized your data in advance. Typically, seasonality is obvious to the human doing the modeling (e.g. sales being up near Christmas), so it's usually preferable for the human to deseasonalize the data in advance using a separate model that bakes in some of their human knowledge of how the world works. Forcing the model to learn seasonal trends fully on its own adds another layer of estimation error.
Prophet is popular because it works off the shelf with non-deseasonalized data and mixed frequency data, which makes it great for quick forecasting exercises. But IMO it is never the ideal model if you have a lot of time and expertise to work with.
Prophet is popular because it works off the shelf with non-deseasonalized data and mixed frequency data, which makes it great for quick forecasting exercises. But IMO it is never the ideal model if you have a lot of time and expertise to work with.