Following the investigation performed in #53, implement a form of cross validation for PyAF models.

Specifications :

1. Cut the dataset in many folds according to a scikit-learn time series split :
   http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
   number of folds => user option (default = 10)

2. To have enough data, use only the last n/2 folds for estimating the models (thanks to forecast R package ;). The default splits look like this :
   [5 ] [6]
   [5 6 ] [7]
   [5 6 7] [8]
   [5 6 7 8] [9]
   [5 6 7 8 9] [10]

3. Use the model decomposition type or formula as a hyperparameter and optimize it. select the decomposition(s) with the lowest mean MAPE on the validation datasets of all the possible splits.

4. Among all the chosen decompositions, select the model with lowest complexity (~ number of inputs)

5. Execute the procedure on the ozone and air passengers datsets and compare with the non-cross validation models (=> 2 jupyter notebooks)