Authors: Ivan Svetunkov
Published at: Open Forecast
Abstract: There are many forecasting related packages in R with varied popularity, the most famous of all being 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 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.
How to cite: Svetunkov (2023). Smooth forecasting with the smooth package in R. OpenForecast.org
The story of the paper: This paper was rejected from the Journal of Statistical Software by a reviewer maintaining the package competing with the
smooth. Given that the paper was written specifically for that journal, and I have nowhere else to submit it, I’ve decided to upload it online and make it freely available.
And here is the smooth hex sticker for completeness. If you need one, get in touch with me.
Thanks for the great effort.
Sorry to hear about the rejection. I was wondering whether you plan on maintaining the package given that it is only some preprint and may get a bit less visibility as it will not be a published papers? This may be important for end users in the long term.
Nothing will change with regards me maintaining the package. I actually plan to introduce a couple of new features in the upcoming version. The publication is in a way irrelevant to the future of the package and is here for completeness :).
Just to make a point, the package has around 30k downloads per month, so it seems to be needed by people. And as long as it is needed, I’ll maintain it.
With don’t you submit your paper to Arxiv? https://arxiv.org/help/submit
Yes, good idea. I’ll do that, thanks!