Man pages for ahaeusser/tscv
Functions and utilities for tidy time series forecasting and time series cross-validation
| acf_vec | Estimate the sample autocorrelation of a numeric vector |
| check_data | Check, convert and shape the input data |
| DSHW | Automatic train a DSHW model |
| elec_load | Hourly electricity load (actual values and forecasts) |
| elec_price | Hourly day-ahead electricity spot prices |
| ELM | Extreme Learning Machine (ELM) |
| estimate_acf | Estimate the sample autocorrelation |
| estimate_kurtosis | Estimate kurtosis |
| estimate_mode | Estimate mode of a distribution based on Kernel Density... |
| estimate_pacf | Estimate the sample partial autocorrelation |
| estimate_skewness | Estimate skewness |
| expand_split | Expand the split_frame |
| EXPERT | Automatic train a EXPERT model |
| file_name | Create a name for a folder or file |
| fitted.DSHW | Extract fitted values from a trained DSHW model |
| fitted.ELM | Extract fitted values from a trained ELM model |
| fitted.EXPERT | Extract fitted values from a trained EXPERT model |
| fitted.MEDIAN | Extract fitted values from a trained median model |
| fitted.MLP | Extract fitted values from a trained MLP model |
| fitted.SMEAN | Extract fitted values from a trained seasonal mean model |
| fitted.SMEDIAN | Extract fitted values from a trained seasonal median model |
| fitted.SNAIVE2 | Extract fitted values from a trained seasonal naive model |
| fitted.TBATS | Extract fitted values from a trained TBATS model |
| forecast.DSHW | Forecast a trained DSHW model |
| forecast.ELM | Forecast a trained ELM model |
| forecast.EXPERT | Forecast a trained EXPERT model |
| forecast.MEDIAN | Forecast a trained median model |
| forecast.MLP | Forecast a trained MLP model |
| forecast.SMEAN | Forecast a trained seasonal mean model |
| forecast.SMEDIAN | Forecast a trained seasonal median model |
| forecast.SNAIVE2 | Forecast a trained seasonal naive model |
| forecast.TBATS | Forecast a trained TBATS model |
| glue_header | Create a header from text |
| grapes-out-grapes | Negated value matching |
| initialize_split | Initialize a plan for train-test split |
| interpolate_missing | Interpolate missing values |
| log_platform | Create platform info |
| log_time | Create string with elapsed time. |
| lst_to_env | Assign objects within a list to an environment |
| M4_monthly_data | Monthly time series data from the M4 Competition |
| M4_quarterly_data | Quarterly time series data from the M4 Competition |
| make_accuracy | Estimate accuracy metrics to evaluate point forecast |
| make_errors | Calculate forecast errors and percentage errors |
| make_future | Convert the forecasts from a 'fable' to a 'future_frame' |
| make_split | Create a split_frame for train and test splits per time... |
| make_tsibble | Convert tibble to tsibble |
| MEDIAN | Median model |
| MLP | Automatic training of MLPs |
| model_sum.DSHW | Provide a succinct summary of a trained DSHW model |
| model_sum.ELM | Provide a succinct summary of a trained ELM model |
| model_sum.EXPERT | Provide a succinct summary of a trained EXPERT model |
| model_sum.MEDIAN | Provide a succinct summary of a trained median model |
| model_sum.MLP | Provide a succinct summary of a trained MLP model |
| model_sum.SMEAN | Provide a succinct summary of a trained seasonal mean model |
| model_sum.SMEDIAN | Provide a succinct summary of a trained seasonal median model |
| model_sum.SNAIVE2 | Provide a succinct summary of a trained seasonal naive model |
| model_sum.TBATS | Provide a succinct summary of a trained TBATS model |
| number_string | Helper function to create numbered strings. |
| pacf_vec | Estimate the sample partial autocorrelation of a numeric... |
| plot_bar | Plot data as bar chart |
| plot_density | Plot the density via Kernel Density Estimator |
| plot_histogram | Plot data as histogram |
| plot_line | Plot data as line chart |
| plot_point | Plot data as scatterplot |
| plot_qq | Quantile-Quantile plot |
| residuals.DSHW | Extract residuals from a trained DSHW model |
| residuals.ELM | Extract residuals from a trained ELM model |
| residuals.EXPERT | Extract residuals from a trained EXPERT model |
| residuals.MEDIAN | Extract residuals from a trained median model |
| residuals.MLP | Extract residuals from a trained MLP model |
| residuals.SMEAN | Extract residuals from a trained seasonal mean model |
| residuals.SMEDIAN | Extract residuals from a trained seasonal median model |
| residuals.SNAIVE2 | Extract residuals from a trained seasonal naive model |
| residuals.TBATS | Extract residuals from a trained TBATS model |
| scale_color_tscv | Color scale constructor for tscv colors. |
| scale_fill_tscv | Fill scale constructor for tscv colors. |
| slice_test | Slice the test data from the complete data |
| slice_train | Slice the train data from the complete data |
| SMEAN | Seasonal mean model |
| SMEDIAN | Seasonal median model |
| smooth_outlier | Identify and replace outliers |
| SNAIVE2 | Seasonal naive model |
| split_index | Create indices for train and test splits. |
| summarise_data | Summary statistics for time series data |
| summarise_split | Summary table of the splitting into training and testing |
| summarise_stats | Summary statistics for time series data |
| TBATS | Automatic train a TBATS model |
| theme_tscv | Custom ggplot2 theme for tscv package |
| theme_tscv_dark | Dark ggplot2 theme for tscv package |
| train_dshw | Double Seasonal Holt-Winters model |
| train_elm | Extreme Learning Machine (ELM) |
| train_expert | EXPERT model |
| train_median | Median model |
| train_mlp | Multilayer Perceptron (MLP) |
| train_smean | Seasonal mean model |
| train_smedian | Seasonal median model |
| train_snaive2 | Seasonal naive model |
| train_tbats | TBATS model |
| tscv_cols | Function to extract tscv colors as hex codes. |
| tscv_pal | Return function to interpolate a tscv color palette. |
