The utsf package provides an engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.
You can install the development version of utsf from GitHub with:
# install.packages("devtools")
devtools::install_github("franciscomartinezdelrio/utsf")This is a basic example which shows you how to solve a common problem:
library(utsf)
# Forecast the next 12 future values of time series UKDriverDeaths using random forest
m <- create_model(UKDriverDeaths, method = "rf")
f <- forecast(m, h = 12)
f$pred # to see the forecast
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1985 1310.838 1238.733 1225.454 1179.955 1272.244 1260.304 1324.537 1346.230
#> Sep Oct Nov Dec
#> 1985 1417.952 1548.769 1715.828 1827.326
library(ggplot2)
autoplot(f)
If you are interested in this package you can read its vignette where all its important features are described.