Linear time series models are commonly used in analyzing dependent data and in forecasting. On the other hand, real phenomena often exhibit nonlinear behavior and the observed data show nonlinear dynamics. This paper introduces the R package NTS that offers various computational tools and nonlinear models for analyzing nonlinear dependent data. The package fills the gaps of several outstanding R packages for nonlinear time series analysis. Specifically, the NTS package covers the implementation of threshold autoregressive (TAR) models, autoregressive conditional mean models with exogenous variables (ACMx), functional autoregressive models, and state-space models. Users can also evaluate and compare the performance of different models and select the best one for prediction. Furthermore, the package implements flexible and comprehensive sequential Monte Carlo methods (also known as particle filters) for modeling non-Gaussian or nonlinear processes. Several examples are used to demonstrate the capabilities of the NTS package.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-016.zip
NTS, nonlinearTseries, NlinTS, nlts, sm, tree, randomForest, TAR, tsDyn, tscount, ftsa, SMC
TimeSeries, MachineLearning, MissingData, Econometrics, Environmetrics, Finance, FunctionalData, SocialSciences
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For attribution, please cite this work as
Liu, et al., "The R Journal: NTS: An R Package for Nonlinear Time Series Analysis", The R Journal, 2021
BibTeX citation
@article{RJ-2021-016,
author = {Liu, Xialu and Chen, Rong and Tsay, Ruey},
title = {The R Journal: NTS: An R Package for Nonlinear Time Series Analysis},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-016},
doi = {10.32614/RJ-2021-016},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {266-292}
}