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 |

ae | Computing the absolute error |

base_ensemble | base_ensemble |

base_ensemble-class | base_ensemble-class |

base_models_loss | Computing the error of base models |

best_mvr | Get best PLS/PCR model |

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 |

build_committee | Building a committee for an ADE model |

combine_predictions | Combining the predictions of several models |

compute_predictions | Compute the predictions of base models |

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 |

EMASE | Weighting Base Models by their Moving Average Squared Error |

embed_timeseries | Embedding a Time Series |

erfc | Complementary Gaussian Error Function |

FIFO | First-In First Out |

forecast | Forecasting using an ensemble predictive model |

get_target | Get the target from a formula |

get_top_models | Extract top learners from their weights |

get_y | Get the response values from a data matrix |

intraining_estimations | Out-of-bag loss estimations |

intraining_predictions | Out-of-bag predictions |

l1apply | Applying lapply on the rows |

learning_base_models | Training the base models of an ensemble |

mae | Computing the mean absolute error |

mase | Computing the mean absolute scaled error |

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 |

mse | Computing the mean squared error |

normalize | Scale a numeric vector using max-min |

predict-methods | Predicting new observations using an ensemble |

proportion | Computing the proportions of a numeric vector |

rbind_l | rbind with do.call syntax |

recent_lambda_observations | Get most recent lambda observations |

rm.null | List without null elements |

rmse | Computing the root mean squared error |

roll_mean_matrix | Computing the rolling mean of the columns of a matrix |

r_squared | Computing R squared |

se | Computing the squared error |

select_best | Selecting best model according to weights |

softmax | Computing the softmax |

split_by | Splitting expressions by pattern |

train_ade | Training procedure of for ADE |

tsensembler | Dynamic Ensembles for Time Series Forecasting |

unlistn | Unlist not using names |

update_ade | Updating an ADE model |

update_ade_meta | Updating the metalearning layer of an ADE model |

update_base_models | Update the base models of an ensemble |

update_weights | Updating the weights of base models |

vcapply | vapply extension for character values |

viapply | vapply extension for integer values |

vlapply | vapply extension for logical values |

vnapply | vapply extension for numeric values |

water_consumption | Water Consumption in Oporto city (Portugal) area. |

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