Package: onlineforecast 1.0.2
onlineforecast: Forecast Modelling for Online Applications
A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "onlineforecast: An R package for adaptive and recursive forecasting" <https://journal.r-project.org/articles/RJ-2023-031/>.
Authors:
onlineforecast_1.0.2.tar.gz
onlineforecast_1.0.2.tar.gz(r-4.5-noble)onlineforecast_1.0.2.tar.gz(r-4.4-noble)
onlineforecast_1.0.2.tgz(r-4.4-emscripten)onlineforecast_1.0.2.tgz(r-4.3-emscripten)
onlineforecast.pdf |onlineforecast.html✨
onlineforecast/json (API)
# Install 'onlineforecast' in R: |
install.packages('onlineforecast', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Dbuilding - Observations and weather forecasts from a single-family building, weather station and Danish Meteorological Institute
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:03eb51a5a1. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 05 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 05 2024 |
Exports:%**%ARas.data.frame.data.listas.data.listas.data.list.data.frameasltaslt.characteraslt.numericaslt.POSIXctaslt.POSIXltbsplinecache_namecache_savecomplete_casescomplete_cases.data.framecomplete_cases.listctct.characterct.numericct.POSIXctct.POSIXltdata.listdepthflattenlistforecastmodelfsgetsegofin_rangeinput_classlagdflagdf.characterlagdf.data.framelagdf.factorlagdf.logicallagdf.matrixlagdf.numericlagdllagveclapply_cbindlapply_cbind_dflapply_rbindlapply_rbind_dflm_fitlm_optimlm_predictlong_formatlplp_vectorlp_vector_cppmake_inputmake_periodicmake_tdaynamsnams<-onepairs.data.listpar_tspbsplinepersistenceplot_tsplot_ts_iseqplot_ts_seriesplot_ts.data.frameplot_ts.data.listplot_ts.matrixplot_ts.rls_fitplotly_tsplotly_ts.data.frameplotly_ts.data.listprint_to_messageprint.forecastmodelpstresampleresample.data.frameresiduals.data.frameresiduals.forecastmodel_fitresiduals.listresiduals.matrixrls_fitrls_optimrls_predictrls_prmrls_summaryrls_updaterls_update_cpprmsescorescore.data.framescore.listsetparstairsstate_getvalstate_setvalstep_optimsubset.data.listsummary.data.listsummary.rls_fit
Dependencies:digestpbsR6RcppRcppArmadillo
Forecast evaluation
Rendered fromforecast-evaluation.Rmd
usingknitr::rmarkdown
on Dec 05 2024.Last update: 2022-05-10
Started: 2020-09-15
Model selection
Rendered frommodel-selection.Rmd
usingknitr::rmarkdown
on Dec 05 2024.Last update: 2022-05-10
Started: 2021-08-21
Setup and use onlineforecast models
Rendered fromsetup-and-use-model.Rmd
usingknitr::rmarkdown
on Dec 05 2024.Last update: 2022-05-10
Started: 2020-09-15
Setup of data for an onlineforecast model
Rendered fromsetup-data.Rmd
usingknitr::rmarkdown
on Dec 05 2024.Last update: 2022-05-10
Started: 2020-09-15