Dynamic Ensembles for Time Series Forecasting

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Documentation for package ‘tsensembler’ version 0.1.0

• DESCRIPTION file.

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