Computes forecasts from historical cutoff points which user can input.If not provided, these are computed beginning from (end - horizon), and working backwards making cutoffs with a spacing of period until initial is reached.
cross_validation(
model,
horizon,
units,
period = NULL,
initial = NULL,
cutoffs = NULL
)Fitted Prophet model.
Integer size of the horizon
String unit of the horizon, e.g., "days", "secs".
Integer amount of time between cutoff dates. Same units as horizon. If not provided, 0.5 * horizon is used.
Integer size of the first training period. If not provided, 3 * horizon is used. Same units as horizon.
Vector of cutoff dates to be used during cross-validtation. If not provided works beginning from (end - horizon), works backwards making cutoffs with a spacing of period until initial is reached.
A dataframe with the forecast, actual value, and cutoff date.
When period is equal to the time interval of the data, this is the technique described in https://robjhyndman.com/hyndsight/tscv/ .