ADE |
Arbitrated Dynamic Ensemble |
base_ensemble |
base_ensemble |
build_base_ensemble |
Wrapper for creating an ensemble |
DETS |
Dynamic Ensemble for Time Series |
embed_timeseries |
Embedding a Time Series |
learning_base_models |
Training the base models of an ensemble |
meta_xgb_predict |
Arbiter predictions via xgb |
model_recent_performance |
Recent performance of models using EMASE |
model_specs |
Setup base learning models |
model_weighting |
Model weighting |
predict |
Predicting new observations using an ensemble |
predict-method |
Predicting new observations using an ensemble |
predict.ade |
Predicting new observations using an ensemble |
predict.base |
Predicting new observations using an ensemble |
predict.dets |
Predicting new observations using an ensemble |
quickADE |
Arbitrated Dynamic Ensemble |
tsensembler |
Dynamic Ensembles for Time Series Forecasting |
update_ade |
Updating an ADE model |
update_ade-method |
Updating an ADE model |
update_ade_meta |
Updating the metalearning layer of an ADE model |
update_ade_meta-method |
Updating the metalearning layer of an ADE model |
update_base_models |
Update the base models of an ensemble |
update_base_models-method |
Update the base models of an ensemble |
update_weights |
Updating the weights of base models |
update_weights-method |
Updating the weights of base models |
water_consumption |
Water Consumption in Oporto city (Portugal) area. |
xgb_optimizer |
XGB optimizer |
xgb_predict_ |
asdasd |