By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon. We specify the forecast horizon ( horizon), and then optionally the size of the initial training period ( initial) and the spacing between cutoff dates ( period). This cross validation procedure can be done automatically for a range of historical cutoffs using the cross_validation function. The Prophet paper gives further description of simulated historical forecasts. This figure illustrates a simulated historical forecast on the Peyton Manning dataset, where the model was fit to an initial history of 5 years, and a forecast was made on a one year horizon. We can then compare the forecasted values to the actual values. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point.
Prophet includes functionality for time series cross validation to measure forecast error using historical data.
Seasonality, Holiday Effects, And Regressors Specifying the locations of the changepoints.Automatic changepoint detection in Prophet.
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