Add XGBoost based models

[antoinecarme]

Need to evaluate models of the type :

Transformed_Signal = Trend + Periodic + XGBoostRegressor(target = PeriodicResidue, input = PeriodicResidue_Lags)

Of course, this is done inside the competition (all possible combinations of transformations, trends and periodics are tested).

[antoinecarme]

Easy to implement,. A generic scikit-learn model can already be used (SVR models are OK).

A new package dependency

pip install xgboost

[antoinecarme]

create anew branch pyaf_xgboost

[antoinecarme]

Time series models based on XGBoost regressors has been added. They are not activated by default.

Need to activate these using something like :

lEngine.mOptions.set_active_autoregressions(['AR', 'XGB']);

[antoinecarme]

Add XGBX models (past of the signal + past of the exogenous variables)

Transformed_Signal = Trend + Periodic + XGBoostRegressor(target = PeriodicResidue, input = PeriodicResidue_Lags + Exogenous_Lags)

[antoinecarme]

Fixed.

Some tests here :

https://github.com/antoinecarme/pyaf/tree/master/tests/xgb

Closing.