tsensembler-package | Dynamic Ensembles for Time Series Forecasting |
ADE | Arbitrated Dynamic Ensemble |
base_ensemble | base_ensemble |
build_base_ensemble | Wrapper for creating an ensemble |
build_committee_set | Build committee set |
constructive_aggregation | Constructive aggregation constructor |
DETS | Dynamic Ensemble for Time Series |
embed_timeseries | Embedding a Time Series |
forecast | Forecasting using an ensemble predictive model |
forecast-method | Forecasting using an ensemble predictive model |
learning_base_models | Training the base models of an ensemble |
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 |
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. |