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forecastML  

Time Series Forecasting with Machine Learning Methods
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("forecastML", "0.9.0")



Attach the package and use:
library("forecastML")
Maintained by
Nickalus Redell
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-09
Latest Update: 2020-05-07
Description:
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" .
How to cite:
Nickalus Redell (2019). forecastML: Time Series Forecasting with Machine Learning Methods. R package version 0.9.0, https://cran.r-project.org/web/packages/forecastML. Accessed 28 Jan. 2025.
Previous versions and publish date:
0.5.0 (2019-10-09 17:30), 0.6.0 (2019-11-23 06:40), 0.7.0 (2020-01-07 11:30), 0.8.0 (2020-02-28 23:40)
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Complete documentation for forecastML
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