R: Dynamic Ensembles for Time Series Forecasting

# Dynamic Ensembles for Time Series Forecasting

## Documentation for package ‘tsensembler’ version 0.1.0

- DESCRIPTION file
- Package README

## Help Pages

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