Man pages for yvesmauron/univariate-time-series-forecasting
Intelligent Algorithms to be Integrated in Business Intelligence solutions.
| accuracy | Summary and detail of accuracy measures |
| accuracy_detail | Details (horizon specific) accuracy measures |
| accuracy_summary | Summary of accuracy measures |
| ae | Absolut error (AE) |
| ape | Absolute percentage error (APE) |
| ase | Absolut scaled error (ASE) |
| auto_arima | Auto Arima |
| auto_ces | Auto Complex exponential Smoothing |
| auto_damped | Auto Damped Exponentional Smoothing |
| auto_dotm | Auto Theta dotm |
| auto_ets | Auto ETS |
| auto_holt | Auto Holt Exponential Smoothing |
| auto_naive | Auto Naive |
| auto_nnar | Auto autoregressive neural networke |
| auto_ses | Auto Simple Exponential Smoothing |
| auto_shd | Auto SHD Combo |
| auto_snaive | Auto seasonal naive |
| auto_theta | Auto Tehta |
| auto_thetaf | Auto thetaf |
| boxcox | BoxCox transformation |
| combine_theta_lines | Theta forecasts |
| date_increment | Increment for date sequence |
| decimal_to_date | Decimal to date |
| diff | Differencing of a vector |
| dot-rx_forecast | Wrapper to forecast with RevoscaleR |
| exp_inverse | Calculates the exponential inverse |
| forecast_forunco | Forunco function for batch forecasting in R |
| forunco | Forunco combination approach |
| generic_combine | Generic combination function |
| generic_forecast | Wrapper to preprecess, predict and postprocess forecasts |
| gmae | Geomatric mean absolute error (GMAE) |
| g_mean | Geometric Mean |
| gmrae | Geomatric mean relative absolut error (GMRAE) |
| inv_boxcox | Inverse Boxcox transformation |
| inv_diff | Inverses diff transformations |
| inverse | Calculates the inverse |
| inv_log | Inverse of log transformation |
| inv_no_pp | Dummy inv PP |
| inv_normalize | Inverses normalization |
| inv_scale | Inverses scaling |
| inv_seasonal_adjustment | Inversed seasonal adjustment |
| is_seasonal | Seasonality test |
| log | Log tranformation |
| mae | Mean absolute error (MAE) |
| mape | Mean absolute percentage error (MAPE) |
| mase | Mean absolut scaled error (MASE) |
| mdrae | Median relative absolut error (MRAE) |
| mrae | Mean relative absolut error (MRAE) |
| mse | Mean square error (MSE) |
| msis | Mean Scaled interval score (MSIS) |
| no_pp | Dummy PP |
| normalize | Normalize vector |
| plot_forunco | Plot of forunco object |
| pool_mean | Calculates the mean of the pool |
| pool_median | Calculates the mean of the pool |
| preprocessor | Preprocessor environment |
| produce_forecasts | Produces forecasts |
| rae | Relative absolut error (RAE) |
| replace_outliers | Replacement of outliers |
| rmse | Root mean square error (MSE) |
| rx_sql_forunco | forunco function for SQL Server compute context |
| sape | Symmetric absolute percentage error (sAPE) |
| sase | Seasonal absolut scaled error (ASE) |
| scale | Scale vector |
| se | Squared error (SE) |
| seasonal_adjustment | Conductes a seasonal adjustment |
| shd | Conventional SHD Combination |
| simple_combination | Simple combination of univariate time series forecasting... |
| smape | Symmetric mean absolute percentage error (sMAPE) |
| smase | Seasonal Mean absolut scaled error (sMASE) |
| squared_inverse | Calculates the squared inverse |
| theta_aea | AEA theta model |
| theta_aem | AEM theta model |
| theta_ala | ALM theta model |
| theta_alm | ALM theta model |
| theta_fit | Theta fit |
| theta_forecast | theta forecast |
| theta_line | Theta line |
| theta_line_zero | Theta line zero |
| theta_mea | MEA theta model |
| theta_mem | MEM theta model |
| theta_mla | MLA theta model |
| theta_mlm | MLM theta model |
| tidier_ts | Tidy time series in data frame |
| time_sequence | Time sequence |
| validation_split | Validation Split |
| weighted_average | Averaging forecasts using weights |
| weight_error | Weighting and pooling methods on the basis of their error |
