PyaF uses a simple separation of the total dataset into estimation/training and test/hold-out datasets (80% and 20% respectively by default, customizable).

Try to evaluate the impact of using cross-validation : gain in model quality/stability/accuracy versus practical aspects (cpu time and memory usage).

Use the "rolling forecasting origin" method described here :

https://www.otexts.org/fpp/2/5