Facebook prophet gives a very high MAPE, how can I improve it? I have some daily sales from 2018-01-01 to 2021-10-21 and I'm trying to predict the sales a year into the future. I opted for facebook prophet. My raw data looks like this:

According to a DF-test, the series is stationary. However according to the documents, prophet does not need stationarity to be efficient. The forecast and fit looks like this:

Clearly prophet is not good at capturing the spikes of the data. Looking at the mean absolute percentage errors over a horizon it just looks horrible:

The mean of these MAPE's is a whopping 53%, and I'm hoping one something at around 5%. Does anyone have a clue on what I can do in order to improve on this model? Obviously if I take the logarithm of the sales the relative error will decrease, but if I inverse transform it back to original it's still quite off in predictions.
EDIT:
Here is the updated forecast with holidays inserted. Seems as if the spikes are better captured but it's several hundreds of thousands of dollars in difference between predicted and actual. I get an RMSE of 1 281 915.

 A: You have suspiciously regular massive spikes sometime about halfway through the third quarter of every year. You don't tell us where your data come from, but if they are US, this is presumably a Black Friday effect. Have you told your model about this very specific predictor?
In general, How to know that your machine learning problem is hopeless? may be helpful. You will need to understand your data and include any relevant predictors you have. Hoping for 5% MAPE may simply be unrealistic.
Also, note that your model will try to separate noise from signal and predict only the signal, with the result that your predictions will vary less than observations. Here is a recent thread on this.
Finally, you may want to take a look at What are the shortcomings of the Mean Absolute Percentage Error (MAPE)? I don't know which objective function Prophet optimizes for, but I assume it is not MAPE. Thus, if your bonus depends on having a low MAPE, you may get closer by post-processing the Prophet forecasts. (I have never seen a business problem that would benefit more from MAPE-optimal forecasts rather than, e.g., MSE- or quantile loss-optimal forecasts. And as you see at that thread, optimal forecasts can depend quite heavily on the evaluation measure.)
