Forecast Modelling for Online Applications
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Documentation for package ‘onlineforecast’ version 1.0.2
Help Pages
| %**% | Multiplication of list with y, elementwise |
| ==.data.list | Determine if two data.lists are identical |
| AR | Auto-Regressive (AR) input |
| as.data.frame.data.list | Convert to data.frame |
| as.data.list | Convert to data.list class |
| as.data.list.data.frame | Convert to data.list class |
| aslt | Convertion to POSIXlt |
| aslt.character | Convertion to POSIXlt |
| aslt.numeric | Convertion to POSIXlt |
| aslt.POSIXct | Convertion to POSIXlt |
| aslt.POSIXlt | Convertion to POSIXlt |
| bspline | Compute base splines of a variable using the R function 'splines::bs', use in the transform stage. |
| cache_name | Generation of a name for a cache file for the value of a function. |
| cache_save | Save a cache file (name generated with 'code_name()' |
| complete_cases | Find complete cases in forecast matrices |
| complete_cases.data.frame | Find complete cases in forecast matrices |
| complete_cases.list | Find complete cases in forecast matrices |
| ct | Convertion to POSIXct |
| ct.character | Convertion to POSIXct |
| ct.numeric | Convertion to POSIXct |
| ct.POSIXct | Convertion to POSIXct |
| ct.POSIXlt | Convertion to POSIXct |
| data.list | Make a data.list |
| Dbuilding | Observations and weather forecasts from a single-family building, weather station and Danish Meteorological Institute (DMI) |
| depth | Depth of a list |
| flattenlist | Flattens list |
| forecastmodel | Class for forecastmodels |
| fs | Generation of Fourrier series. |
| getse | Getting subelement from list. |
| gof | Simple wrapper for graphics.off() |
| input_class | Class for forecastmodel inputs |
| in_range | Selects a period |
| lagdf | Lagging which returns a data.frame |
| lagdf.character | Lagging which returns a data.frame |
| lagdf.data.frame | Lagging which returns a data.frame |
| lagdf.factor | Lagging which returns a data.frame |
| lagdf.logical | Lagging which returns a data.frame |
| lagdf.matrix | Lagging which returns a data.frame |
| lagdf.numeric | Lagging which returns a data.frame |
| lagdl | Lagging which returns a data.list |
| lagvec | Lag by shifting |
| lapply_cbind | Helper which does lapply and then cbind |
| lapply_cbind_df | Helper which does lapply, cbind and then as.data.frame |
| lapply_rbind | Helper which does lapply and then rbind |
| lapply_rbind_df | Helper which does lapply, rbind and then as.data.frame |
| lm_fit | Fit an onlineforecast model with 'lm' |
| lm_optim | Optimize parameters for onlineforecast model fitted with LM |
| lm_predict | Prediction with an lm forecast model. |
| long_format | Long format of prediction data.frame |
| lp | First-order low-pass filtering |
| lp_vector | First-order low-pass filtering |
| lp_vector_cpp | Low pass filtering of a vector. |
| make_input | Make a forecast matrix (as data.frame) from observations. |
| make_periodic | Make an forecast matrix with a periodic time signal. |
| make_tday | Make an hour-of-day forecast matrix |
| nams | Return the column names |
| nams<- | Return the column names |
| one | Create ones for model input intercept |
| pairs.data.list | Generation of pairs plot for a data.list. |
| par_ts | Set parameters for 'plot_ts()' |
| pbspline | Wrapper for 'bspline' with 'periodic=TRUE' |
| persistence | Generate persistence forecasts |
| plotly_ts | Time series plotting |
| plotly_ts.data.frame | Time series plotting |
| plotly_ts.data.list | Time series plotting |
| plot_ts | Time series plotting |
| plot_ts.data.frame | Time series plotting |
| plot_ts.data.list | Time series plotting |
| plot_ts.matrix | Time series plotting |
| plot_ts.rls_fit | Time series plotting |
| plot_ts_iseq | Time series plotting |
| plot_ts_series | Time series plotting |
| print.forecastmodel | Print forecast model |
| print_to_message | Simple function for capturing from the print function and send it in a message(). |
| pst | Simple wrapper for paste0(). |
| resample | Resampling to equidistant time series |
| resample.data.frame | Resampling to equidistant time series |
| residuals.data.frame | Calculate the residuals given a forecast matrix and the observations. |
| residuals.forecastmodel_fit | Calculate the residuals given a forecast matrix and the observations. |
| residuals.list | Calculate the residuals given a forecast matrix and the observations. |
| residuals.matrix | Calculate the residuals given a forecast matrix and the observations. |
| rls_fit | Fit an onlineforecast model with Recursive Least Squares (RLS). |
| rls_optim | Optimize parameters for onlineforecast model fitted with RLS |
| rls_predict | Prediction with an rls model. |
| rls_prm | Function for generating the parameters for RLS regression |
| rls_summary | Print summary of an onlineforecast model fitted with RLS |
| rls_update | Updates the model fits |
| rls_update_cpp | Calculating k-step recursive least squares estimates |
| rmse | Computes the RMSE score. |
| score | Calculate the score for each horizon. |
| score.data.frame | Calculate the score for each horizon. |
| score.list | Calculate the score for each horizon. |
| setpar | Setting 'par()' plotting parameters |
| stairs | Plotting stairs with time point at end of interval. |
| state_getval | Get the state value kept in last call. |
| state_setval | Set a state value to be kept for next the transformation function is called. |
| step_optim | Forward and backward model selection |
| subset.data.list | Take a subset of a data.list. |
| summary.data.list | Summary with checks of the data.list for appropriate form. |
| summary.rls_fit | Print summary of an onlineforecast model fitted with RLS |