TISEAN

Practical implementation of nonlinear time series methods: The TISEAN package. We describe the implementation of methods of nonlinear time series analysis which are based on the paradigm of deterministic chaos. A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters. Computer programs that implement the resulting strategies are publicly available as the TISEAN software package. The use of each algorithm will be illustrated with a typical application. As to the theoretical background, we will essentially give pointers to the literature.


References in zbMATH (referenced in 132 articles , 1 standard article )

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  1. Danca, Marius-F.; Kuznetsov, Nikolay: Matlab code for Lyapunov exponents of fractional-order systems (2018)
  2. Ma, Huanfei; Leng, Siyang; Chen, Luonan: Data-based prediction and causality inference of nonlinear dynamics (2018)
  3. Alves, Paulo Ricardo L.; Duarte, L. G. S.; da Mota, L. A. C. P.: A new characterization of chaos from a time series (2017)
  4. Udhayakumar, Radhagayathri K.; Karmakar, Chandan; Palaniswami, Marimuthu: Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal (2017)
  5. Kuznetsov, N. V.: The Lyapunov dimension and its estimation via the Leonov method (2016)
  6. Kuznetsov, N. V.; Alexeeva, T. A.; Leonov, G. A.: Invariance of Lyapunov exponents and Lyapunov dimension for regular and irregular linearizations (2016)
  7. Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso: Data based identification and prediction of nonlinear and complex dynamical systems (2016)
  8. Zambrano-Serrano, E.; Campos-Cantón, E.; Muñoz-Pacheco, J. M.: Strange attractors generated by a fractional order switching system and its topological horseshoe (2016)
  9. Bradley, Elizabeth; Kantz, Holger: Nonlinear time-series analysis revisited (2015)
  10. Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen: Unified functional network and nonlinear time series analysis for complex systems science: the pyunicorn package (2015)
  11. Jonathan F. Donges, Jobst Heitzig, Boyan Beronov, Marc Wiedermann, Jakob Runge, Qing Yi Feng, Liubov Tupikina, Veronika Stolbova, Reik V. Donner, Norbert Marwan, Henk A. Dijkstra, J. Kurths: Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package (2015) arXiv
  12. McCullough, Michael; Small, Michael; Stemler, Thomas; Iu, Herbert Ho-Ching: Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems (2015)
  13. Thomas, Robin D.; Moses, Nathan C.; Semple, Erin A.; Strang, Adam J.: An efficient algorithm for the computation of average mutual information: validation and implementation in Matlab (2014)
  14. Kantz, Holger; Radons, Günter; Yang, Hongliu: The problem of spurious Lyapunov exponents in time series analysis and its solution by covariant Lyapunov vectors (2013)
  15. Maus, A.; Sprott, J. C.: Evaluating Lyapunov exponent spectra with neural networks (2013)
  16. Mera, Maria Eugenia; Morán, Manuel: Error covariance matrix estimation of noisy and dynamically coupled time series (2013)
  17. Nomura, Taishin; Oshikawa, Shota; Suzuki, Yasuyuki; Kiyono, Ken; Morasso, Pietro: Modeling human postural sway using an intermittent control and hemodynamic perturbations (2013)
  18. Çoban, Gürsan; Büyüklü, Ali H.; Das, Atin: A linearization based non-iterative approach to measure the Gaussian noise level for chaotic time series (2012)
  19. Christodoulou, Eleni G.; Sakkalis, Vangelis; Tsiaras, Vassilis; Tollis, Ioannis G.: BrainNetVis: an open-access tool to effectively quantify and visualize brain networks (2011) ioport
  20. Dafilis, Mathew P.; Sinclair, Nicholas C.; Cadusch, Peter J.; Liley, David T. J.: Re-evaluating the performance of the nonlinear prediction error for the detection of deterministic dynamics (2011)

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