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ForecastingEnsembles  

Time Series Forecasting Using 23 Individual Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("ForecastingEnsembles", "0.5.0")



Attach the package and use:
library("ForecastingEnsembles")
Maintained by
Russ Conte
[Scholar Profile | Author Map]
First Published: 2025-04-01
Latest Update: 2025-04-01
Description:
Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.
How to cite:
Russ Conte (2025). ForecastingEnsembles: Time Series Forecasting Using 23 Individual Models. R package version 0.5.0, https://cran.r-project.org/web/packages/ForecastingEnsembles. Accessed 05 May. 2025.
Previous versions and publish date:
No previous versions
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Complete documentation for ForecastingEnsembles
Functions, R codes and Examples using the ForecastingEnsembles R package
Full ForecastingEnsembles package functions and examples
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