Man pages for vcerqueira/tsensembler
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 |
| arima_predict | Predict function for a model created from bm_arima |
| 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_arima | Fitting an ARIMA model Using function *auto.arima* from... |
| bm_cubist | Fit Cubist models (M5) |
| bm_ets | Fitting an Exponential Smoothing model Using function *ets*... |
| 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 |
| bm_tbats | Fitting an tbats model Using function *tbats* from forecast... |
| bm_timeseries | Classical time series models |
| bm_xgb | xgb base model |
| build_base_ensemble | Wrapper for creating an ensemble |
| build_committee | Building a committee for an ADE model |
| build_committee_set | Build committee set |
| CA.ADE_hat | CA generaliser using arbitrage |
| CA.EWA_hat | CA generaliser using exponentially weighted average |
| CA.FixedShare_hat | CA generaliser using fixed share |
| CA.MLpol_hat | CA generaliser using polynomial weighted average |
| CA.OGD_hat | CA generaliser using OGD |
| CA.Ridge_hat | CA generaliser using ridge regression |
| combine_committees | Merge across sub-ensembles |
| combine_predictions | Combining the predictions of several models |
| committee_set-class | committee_set-class |
| compute_predictions | Compute the predictions of base models |
| constructive_aggregation | Constructive aggregation constructor |
| constructive_aggregation_ | constructive_aggregation_ |
| constructive_aggregation-class | constructive_aggregation-class |
| contiguous_count | Contiguity check |
| 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 |
| ets_predict | Predict function for a model created from bm_ets |
| 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 |
| hat_info | Get predict data for generalising |
| holdout | Holdout |
| 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 |
| loss_meta_learn | Training an arbiter |
| merging_in_experts | Merge models in each committee |
| meta_cubist | Training a RBR arbiter |
| meta_cubist_predict | Arbiter predictions via Cubist |
| meta_gp | Training a Gaussian process arbiter |
| meta_gp_predict | Arbiter predictions via linear model |
| meta_lasso | Training a LASSO arbiter |
| meta_lasso_predict | Arbiter predictions via linear model |
| meta_predict | Predicting loss using arbiter |
| meta_rf | Training a random forest arbiter |
| meta_rf_predict | Arbiter predictions via ranger |
| 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 |
| multi_step_predict | multi step prediction s |
| multi_step_predict-base_ensemble-method | multi step prediction |
| normalize | Scale a numeric vector using max-min |
| predict-constructive_aggregation-method | predict method for constructive aggregation |
| predict-methods | Predicting new observations using an ensemble |
| predict_pls_pcr | predict method for pls/pcr |
| proportion | Computing the proportions of a numeric vector |
| prune_c_contiguity | Prune subsets by contiguity |
| prune_c_outperformance | Prune subsets by out-performance |
| 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 |
| se | Computing the squared error |
| select_best | Selecting best model according to weights |
| sequential_reweighting | Sequential Re-weighting for controlling predictions'... |
| sliding_similarity | Sliding similarity via Pearson's correlation |
| soft.completion | Soft Imputation |
| softmax | Computing the softmax |
| split_by | Splitting expressions by pattern |
| tbats_predict | Predict function for a model created from bm_tbats |
| 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. |
| xgb_predict | xgb predict fun |
