Dynamic Ensembles for Time Series Forecasting


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Documentation for package ‘tsensembler’ version 0.0.4

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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.