Man pages for tsensembler
Dynamic Ensembles for Time Series Forecasting

ADEArbitrated Dynamic Ensemble
ADE-classArbitrated Dynamic Ensemble
ade_hatPredictions by an ADE ensemble
ade_hat-classPredictions by an ADE ensemble
aeComputing the absolute error
base_ensemblebase_ensemble
base_ensemble-classbase_ensemble-class
base_models_lossComputing the error of base models
best_mvrGet best PLS/PCR model
blocked_prequentialPrequential Procedure in Blocks
bm_cubistFit Cubist models (M5)
bm_ffnnFit Feedforward Neural Networks models
bm_gaussianprocessFit Gaussian Process models
bm_gbmFit Generalized Boosted Regression models
bm_glmFit Generalized Linear Models
bm_marsFit Multivariate Adaptive Regression Splines models
bm_pls_pcrFit PLS/PCR regression models
bm_pprFit Projection Pursuit Regression models
bm_randomforestFit Random Forest models
bm_svrFit Support Vector Regression models
build_base_ensembleWrapper for creating an ensemble
build_committeeBuilding a committee for an ADE model
combine_predictionsCombining the predictions of several models
compute_predictionsCompute the predictions of base models
DETSDynamic Ensemble for Time Series
DETS-classDynamic Ensemble for Time Series
dets_hatPredictions by an DETS ensemble
dets_hat-classPredictions by an DETS ensemble
EMASEWeighting Base Models by their Moving Average Squared Error
embed_timeseriesEmbedding a Time Series
erfcComplementary Gaussian Error Function
FIFOFirst-In First Out
forecastForecasting using an ensemble predictive model
get_targetGet the target from a formula
get_top_modelsExtract top learners from their weights
get_yGet the response values from a data matrix
intraining_estimationsOut-of-bag loss estimations
intraining_predictionsOut-of-bag predictions
l1applyApplying lapply on the rows
learning_base_modelsTraining the base models of an ensemble
maeComputing the mean absolute error
maseComputing the mean absolute scaled error
model_recent_performanceRecent performance of models using EMASE
model_specsSetup base learning models
model_specs-classSetup base learning models
model_weightingModel weighting
mseComputing the mean squared error
normalizeScale a numeric vector using max-min
predict-methodsPredicting new observations using an ensemble
proportionComputing the proportions of a numeric vector
rbind_lrbind with do.call syntax
recent_lambda_observationsGet most recent lambda observations
rm.nullList without null elements
rmseComputing the root mean squared error
roll_mean_matrixComputing the rolling mean of the columns of a matrix
r_squaredComputing R squared
seComputing the squared error
select_bestSelecting best model according to weights
softmaxComputing the softmax
split_bySplitting expressions by pattern
train_adeTraining procedure of for ADE
tsensemblerDynamic Ensembles for Time Series Forecasting
unlistnUnlist not using names
update_adeUpdating an ADE model
update_ade_metaUpdating the metalearning layer of an ADE model
update_base_modelsUpdate the base models of an ensemble
update_weightsUpdating the weights of base models
vcapplyvapply extension for character values
viapplyvapply extension for integer values
vlapplyvapply extension for logical values
vnapplyvapply extension for numeric values
water_consumptionWater Consumption in Oporto city (Portugal) area.
tsensembler documentation built on Aug. 28, 2017, 5:02 p.m.