This paper is an introduction to the new package in R called smoots (smoothing time series), developed for data-driven local polynomial smoothing of trend-stationary time series. Functions for data-driven estimation of the first and second derivatives of the trend are also built-in. It is first applied to monthly changes of the global temperature. The quarterly US-GDP series shows that this package can also be well applied to a semiparametric multiplicative component model for non-negative time series via the log-transformation. Furthermore, we introduced a semiparametric Log-GARCH and a semiparametric Log-ACD model, which can be easily estimated by the smoots package. Of course, this package applies to suitable time series from any other research area. The smoots package also provides a useful tool for teaching time series analysis, because many practical time series follow an additive or a multiplicative component model.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-017.zip
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For attribution, please cite this work as
Feng, et al., "The R Journal: The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series", The R Journal, 2022
BibTeX citation
@article{RJ-2022-017,
author = {Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik},
title = {The R Journal: The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-017},
doi = {10.32614/RJ-2022-017},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {182-195}
}