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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("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 AirPassengers using random forest
f <- forecast(AirPassengers, h = 12, method = "rf")
f$pred # to see the forecast
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1961 453.9954 446.4064 473.0415 489.7651 510.1506 574.9026 636.7767 640.0431
#> Sep Oct Nov Dec
#> 1961 550.2684 500.6805 451.3721 477.6338library(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.