Time series models (e.g. Arima() and ets()) use stats::ts() or forecast::msts()
to apply seasonality. We can do the same process using the following
general time series parameter:
It's usually best practice to not tune this parameter, but rather set
to obvious values based on the seasonality of the data:
Daily Seasonality: Often used with hourly data (e.g. 24 hourly timestamps per day)
Weekly Seasonality: Often used with daily data (e.g. 7 daily timestamps per week)
Yearly Seasonalty: Often used with weekly, monthly, and quarterly data
(e.g. 12 monthly observations per year).
However, in the event that users want to experiment with period tuning, you
can do so with seasonal_period().