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
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Feng, et al., "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 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} }