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onlineforecast  

Forecast Modelling for Online Applications
View on CRAN: Click here


Download and install onlineforecast package within the R console
Install from CRAN:
install.packages("onlineforecast")

Install from Github:
library("remotes")
install_github("cran/onlineforecast")

Install by package version:
library("remotes")
install_version("onlineforecast", "1.0.2")



Attach the package and use:
library("onlineforecast")
Maintained by
Peder Bacher
[Scholar Profile | Author Map]
First Published: 2020-09-15
Latest Update: 2022-05-10
Description:
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 and the paper "onlineforecast: An R package for adaptive and recursive forecasting" .
How to cite:
Peder Bacher (2020). onlineforecast: Forecast Modelling for Online Applications. R package version 1.0.2, https://cran.r-project.org/web/packages/onlineforecast. Accessed 07 Apr. 2025.
Previous versions and publish date:
0.9.3 (2020-09-15 10:50), 0.10.0 (2021-08-21 14:40), 1.0.0 (2021-09-30 11:50), 1.0.1 (2022-05-10 11:30)
Other packages that cited onlineforecast R package
View onlineforecast citation profile
Other R packages that onlineforecast depends, imports, suggests or enhances
Complete documentation for onlineforecast
Functions, R codes and Examples using the onlineforecast R package
Some associated functions: AR . Dbuilding . as.data.frame.data.list . as.data.list . aslt . bs . cache_name . cache_save . complete_cases . ct . data.list . depth . equals-.data.list . flattenlist . forecastmodel . fs . getse . gof . grapes-times-times-grapes . in_range . input_class . lagdf.character . lagdf.factor . lagdf.logical . lagdf.matrix . lagdf.numeric . lagdf . lagdl . lagvec . lapply_cbind . lapply_cbind_df . lapply_rbind . lapply_rbind_df . lm_fit . lm_optim . lm_predict . long_format . lp . lp_vector . lp_vector_cpp . make_input . make_periodic . make_tday . nams . one . onlineforecast-package . pairs.data.list . par_ts . pbspline . persistence . plot_ts . plotly_ts.data.frame . plotly_ts.data.list . print.forecastmodel . print_to_message . pst . resample.data.frame . resample . residuals . rls_fit . rls_optim . rls_predict . rls_prm . rls_summary . rls_update . rls_update_cpp . rmse . score . setpar . stairs . state_getval . state_setval . step_optim . subset.data.list . summary.data.list . summary.rls_fit . 
Some associated R codes: AR.R . RcppExports.R . as.data.list.R . aslt.R . bspline.R . cache_name.R . cache_save.R . complete_cases.R . ct.R . data.R . data.list.R . depth.R . extra.R . flattenlist.R . forecastmodel.R . forecastmodel.R-documentation.R . fs.R . getse.R . gof.R . in_range.R . input_class.R . input_class.R-documentation.R . lagdf.R . lagdl.R . lagvec.R . lapply.R . lm_fit.R . lm_optim.R . lm_predict.R . long_format.R . lp.R . make_input.R . make_periodic.R . make_tday.R . nams.R . ones.R . onlineforecast-package.R . operator_multiply.R . par_ts.R . persistence.R . plot_ts.R . plotly_ts.R . print_to_message.R . pst.R . resample.R . residuals.R . rls_fit.R . rls_optim.R . rls_predict.R . rls_prm.R . rls_summary.R . rls_update.R . rmse.R . score.R . setpar.R . stairs.R . state_getval.R . state_setval.R . step_optim.R .  Full onlineforecast package functions and examples
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