Man pages for 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 |
| 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 |
| bm_xgb | Base model for XGBoost |
| 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 |
| 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 |
| 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 |
| meta_cubist | Training a RBR arbiter |
| meta_cubist_predict | Arbiter predictions via Cubist |
| meta_ffnn | Training a Gaussian prosadacess arbiter |
| meta_ffnn_predict | Arbiter predictions via linear ssmodel |
| 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_mars | Training a meta_mars process arbiter |
| meta_mars_predict | Arbiter predictions via mars model |
| meta_pls | Training a pls process arbiter |
| meta_pls_predict | Arbiter predictions via pls model |
| meta_ppr | Training a meta_mars process arbiter |
| meta_ppr_predict | Arbiter predictions via ppr model |
| meta_predict | Predicting loss using arbiter |
| meta_rf | Training a random forest arbiter |
| meta_rf_predict | Arbiter predictions via ranger |
| meta_svr | Training a Gaussian process arbiter |
| meta_svr_predict | Arbiter predictions via linear model |
| meta_xgb | Training a xgb arbiter |
| meta_xgb_predict | Arbiter predictions via xgb |
| 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 |
| predict_pls_pcr | predict method for pls/pcr |
| proportion | Computing the proportions of a numeric vector |
| rbind_l | rbind with do.call syntax |
| recent_lambda_observations | Get most recent lambda observations |
| 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 |
| train_ade | Training procedure of for ADE |
| train_ade_quick | ADE training poor version Train meta-models in the training... |
| tsensembler | Dynamic Ensembles for Time Series Forecasting |
| 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 |
| water_consumption | Water Consumption in Oporto city (Portugal) area. |
| xgb_optimizer | XGB optimizer |
| xgb_predict | XGBoost predict function |
| xgb_predict_ | asdasd |
