Man pages for vcerqueira/vest
automatic feature eng for forecasting
| accelaration | time series acceleration |
| apply_transformations | Apply transformations generic |
| apply_transformations-VEST-method | Apply transformations using VEST |
| arima_multistep | ARIMA MODEL |
| BayesianSignTest | Bayes Sign Test |
| cleanup_feats | Feature clean up generic |
| cleanup_feats-VEST-method | Feature clean up method |
| convert.fft | Convert FFT |
| cospi | cosine sad |
| create_datasets | Create train/test data sets |
| dauDWTenergy | Daubechies DWT |
| direct_msf | Direct multi-step forecasting |
| estimate_fi | Importance Scores |
| ets_multistep | ETS MODEL |
| feat_select_bf | Feature Selection, best summary by rep2 |
| feat_select_br | Feature Selection, best rep |
| feat_select_corr | Feature Selection, best summary by rep3 |
| feat_select_vest | Feature Selection, best summary by rep |
| feature_engineering | Feature Engineering Wrapper |
| fft_strength | FFT AMP |
| ft_summary | Features summary |
| ft_t_boxcox | Boxcox |
| ft_t_diff | First Differences transf. |
| ft_t_diff2 | Second Differences transf. |
| ft_t_dwt | DWT transf. |
| ft_t_fourier_cos | First Differsssaassadences transf. |
| ft_t_fourier_sin | First Differences transf.asdada |
| ft_transformation | Feature transformations |
| ft_t_sdiff | Seasonal differencing |
| ft_t_seasadj | Seasonal adjustment |
| ft_t_seascomp | Seasonal component |
| ft_t_sma | SMA transf. |
| ft_t_wins | Winsorization transf. |
| get_fourier_terms | Dynamic Harmonic Regre. Terms |
| get_fourier_terms_ | Get fourieer terms for test |
| get_importance | Get importance generic |
| get_importance-VEST-method | Get importance scores |
| get_outer_dynamics | Get outer dynamics DHR |
| get_scale_keys | Get scaling factors |
| get_summary | Get summary operations generic |
| get_summary-VEST-method | Get summary operations |
| get_transformations | Get transformations generic fun |
| get_transformations-VEST-method | Transform the original representation |
| get_xgb_model | Create xgb model using a grid search |
| get_xgb_preds | Xgb predictions |
| get_y_val | Get target vector |
| HURST | Hurst exponent |
| K_HAT | Embed dim. estimation |
| LASSO.predict | glme pred |
| LASSO.train | lasso |
| log_trans | log transform |
| M5.cycle | wrappersadda |
| M5.multi_step_cycle | Wrapper M5 multistep cycle |
| M5.predict | Model tree prediction function |
| M5.self_cycle | wrapperq |
| M5.train | Model tree using cubist |
| max_lyapunov_exp | Max lyapunov exponent |
| multistep.prediction | Multi step prediction with updated dynamics |
| my_embedd | Embedding function |
| myholdout2 | holdout |
| nout2 | no outliers |
| npeaks | N peaks |
| percentual_difference | perc difference |
| poincare_variability | Poincare variability |
| pred_fourier_terms | Predicting new DHR terms |
| predict_outer_dynamics | Predicting new outer dynamics |
| predict-VEST-method | Predict feature values with new observations |
| proportion | normalize |
| relative_dispersion | Relative dispersion |
| rep_holdout_origins | rep h origins |
| replace_inf | replace infs |
| run_classical_methods | run clasical methds |
| search_k_vall | Search best embedding dimension using model tree performance |
| sinpi | sine sad |
| slope | Slope |
| soft_completion | Soft completion |
| step_change | Step change |
| tbats_multistep | TBATS MODEL |
| tpoints | Turning Points |
| ts_df_slip_by_size | Splitting data |
| ts_holdout | Holdout estimation |
| unroll_embedd | Unroll embedded time series |
| VEST | Data.frame constructor for VEST class |
| VEST-class | Class for time series feature eng. |
| WF_part1 | WF part 1 Getting the VEST features |
| WF_part2 | WF part2 |
| WF_part2_direct2 | Workflow part 2 Training forecasting models MSF with a direct... |
| xgb.optimize | Optimizing xgb |
| XGB.predict | XGB predict function |
| XGB.train | XGB training function |
