Dynamic Ensembles for Time Series Forecasting

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Documentation for package ‘tsensembler’ version 0.0.2

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tsensembler-package Dynamic Ensembles for Time Series Forecasting
ADE Arbitrated Dynamic Ensemble
ADE-class Arbitrated Dynamic Ensemble
ade_hat Predictions by an ADE ensemble
ade_hat-class Predictions by an ADE ensemble
base_ensemble base_ensemble
blocked_prequential Prequential Procedure in Blocks
bm_cubist Fit Cubist models (M5)
bm_ffnn Fit Feedforward Neural Networks models
bm_gaussianprocess Fit Gaussian Process models
bm_gbm Fit Generalized Boosted Regression models
bm_glm Fit Generalized Linear Models
bm_mars Fit Multivariate Adaptive Regression Splines models
bm_pls_pcr Fit PLS/PCR regression models
bm_ppr Fit Projection Pursuit Regression models
bm_randomforest Fit Random Forest models
bm_svr Fit Support Vector Regression models
build_base_ensemble Wrapper for creating an ensemble
DETS Dynamic Ensemble for Time Series
DETS-class Dynamic Ensemble for Time Series
dets_hat Predictions by an DETS ensemble
dets_hat-class Predictions by an DETS ensemble
embed_timeseries Embedding a Time Series
erfc Complementary Gaussian Error Function
forecast Forecasting using an ensemble predictive model
forecast-method Forecasting using an ensemble predictive model
get_y Get the response values from a data matrix
intraining_estimations Out-of-bag loss estimations
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_specs-class 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
roll_mean_matrix Computing the rolling mean of the columns of a matrix
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.