The R Journal: accepted article

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DChaos: An R Package for Chaotic Time Series Analysis PDF download
Julio E. Sandubete and Lorenzo Escot

Abstract Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory is the detection of a chaotic behaviour from empirical time-series data. One of the main features of chaos is the well-known initial-value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial-value sensitive property estimating the so-called Lyapunov exponents. This paper describes the main estimation methods of the Lyapunov exponent from time-series data. At the same time we present the DChaos library. R users may computes the delayed-coordinate embedding vector from time-series data, estimates the best-fitted neural net model from the delayed-coordinate embedding vectors, calculates analytically the partial derivatives from the chosen neural nets model. They can also obtain the neural net estimator of the Lyapunov exponent from the partial derivatives computed previously by two different procedures and four ways of subsampling by blocks. To sum up the DChaos package allows the R users to test robustly the hypothesis of chaos in order to know if the data-generating process behind time series behave chaotically or not. The package’s functionality is illustrated by examples.

Received: 2020-06-03; online 2021-06-07
CRAN packages: tseriesChaos, nonlinearTseries, fNonlinear, DChaos, highfrequency, nnet
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, Econometrics, MachineLearning, SocialSciences


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@article{RJ-2021-036,
  author = {Julio E. Sandubete and Lorenzo Escot},
  title = {{DChaos: An R Package for Chaotic Time Series Analysis}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-036},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-036/index.html}
}