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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("pak")
pak::pak("franciscomartinezdelrio/utsf")or you can install the stable version from CRAN:
install.packages("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 1299.725 1215.264 1227.347 1170.516 1261.783 1263.307 1325.378 1346.959
#> Sep Oct Nov Dec
#> 1985 1412.481 1543.708 1698.649 1805.963
library(ggplot2)
autoplot(f)
If you are interested in this package you can read its vignette where all its important features are described.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.