  There are many forecasting related packages in R with varied popularity, the
most famous of all being \texttt{forecast}, which implements several important
forecasting approaches, such as ARIMA, ETS, TBATS and others. However, the main
issue with the existing functionality is the lack of flexibility for research
purposes, when it comes to modifying the implemented models. The R package
\texttt{smooth} introduces a new approach to univariate forecasting,
implementing ETS and ARIMA models in Single Source of Error (SSOE) state space
form and implementing an advanced functionality for experiments and time series
analysis. It builds upon the SSOE model and extends it by including explanatory
variables, multiple frequencies, and introducing advanced forecasting
instruments. In this paper, we explain the philosophy behind the package and
show how the main functions work.

Methodology;Mathematical Software;Applications