fasster | Additive Switching of Seasonality , Trend and Exogenous | Time Series Database library

After many failed attempts I managed to solve this problem using this code:

The fasster package currently doesn't provide any facilities for automatic model selection (https://github.com/tidyverts/fasster/issues/50).

To identify an appropriate fasster model specification, you can start by graphically exploring your data to identify its structure. Some questions you may consider include:

• Is your data seasonal? Which seasonal periods are required?
  Include seasonality with fourier terms via fourier(period, K) or season(period). Generally using fourier() terms are better, as being able to specify the number of harmonics (K) allows you to control the smoothness of the seasonality and reduce model parameters.
• Does your data include an level or local trends?
  Include a level with poly(1) or a trend with poly(2).
• Are there potential exogenous regressors (a good example of this is temperature in electricity demand).
  Include exogenous regressors in the same way as you would in lm().
• Do the patterns in your data alternate in predictable ways (for example, seasonality on weekdays and weekends.)
  Use %S% to switch between these patterns. For example to have a different seasonal pattern for weekdays and weekends you may consider day_type %S% (fourier("day", K = 7)), where day_type is a variable in your model that specifies if the day is a weekday or weekend.

A simple approach to capturing the increase in patients after a holiday would be to include DaysAfterHoliday as an exogenous regressor. As this relationship is likely non-linear, you may need to also include some non-linear transformations of this variable as exogenous regressors.