The causality is an important concept that is widely studied in the literature, and has several applications, especially when modelling dependencies within complex data, such as multivariate time series. In this article, we present a theoretical description of methods from the NlinTS package, and we focus on causality measures. The package contains the classical Granger causality test. To handle non-linear time series, we propose an extension of this test using an artificial neural network. The package includes an implementation of the Transfer entropy, which is also considered as a non linear causality measure based on information theory. For discrete variables, we use the classical Shannon Transfer entropy, while for continuous variables, we adopt the k-nearest neighbors approach to estimate it.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-016.zip
NlinTS, vars, lmtest, RTransferEntropy, timeSeries, Rcpp
TimeSeries, Finance, Econometrics, HighPerformanceComputing, MissingData, NumericalMathematics, SocialSciences
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
Hmamouche, "NlinTS: An R Package For Causality Detection in Time Series", The R Journal, 2020
BibTeX citation
@article{RJ-2020-016,
author = {Hmamouche, Youssef},
title = {NlinTS: An R Package For Causality Detection in Time Series},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-016},
doi = {10.32614/RJ-2020-016},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {21-31}
}