from metats.pipeline import MetaLearning

pipeline = MetaLearning(method='selection')

# feature extraction using deep auto encoders
pipeline.add_feature(lstm_auto_encoder)
# statistical features
pipeline.add_feature(tsfresh_feature_generator)

# adding a few base forecasters
pipeline.add_forecaster(arima_forecaster)
pipeline.add_forecaster(ets_forecaster)
pipeline.add_forecaster(xgboost_forecaster)

# define a meta-learner
pipeline.add_metalearner(RandomForestClassifier())
# training the models in the pipeline
pipeline.fit(data, fh=7)