Increasing the number of Fourier components allows the seasonality to change more quickly (at risk of overfitting). Default values for yearly and weekly seasonalities are 10 and 3 respectively.
add_seasonality(
m,
name,
period,
fourier.order,
prior.scale = NULL,
mode = NULL,
condition.name = NULL
)Prophet object.
String name of the seasonality component.
Float number of days in one period.
Int number of Fourier components to use.
Optional float prior scale for this component.
Optional 'additive' or 'multiplicative'.
String name of the seasonality condition.
The prophet model with the seasonality added.
Increasing prior scale will allow this seasonality component more flexibility, decreasing will dampen it. If not provided, will use the seasonality.prior.scale provided on Prophet initialization (defaults to 10).
Mode can be specified as either 'additive' or 'multiplicative'. If not specified, m$seasonality.mode will be used (defaults to 'additive'). Additive means the seasonality will be added to the trend, multiplicative means it will multiply the trend.
If condition.name is provided, the dataframe passed to `fit` and `predict` should have a column with the specified condition.name containing booleans which decides when to apply seasonality.