Smooth forecasting with the smooth package in R

TL;DR

The paper introduces the R package 'smooth' which offers flexible, advanced univariate forecasting models in SSOE form, facilitating research and experimentation with time series data.

Contribution

The 'smooth' package extends existing forecasting tools by enabling model modifications, incorporating explanatory variables, multiple frequencies, and advanced instruments within the SSOE framework.

Findings

Provides flexible modeling for research purposes

Supports inclusion of explanatory variables and multiple frequencies

Enhances experimentation with forecasting models

Abstract

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.