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 118 articles , 1 standard article )

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  1. 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)
  2. Kantz, Holger; Radons, Günter; Yang, Hongliu: The problem of spurious Lyapunov exponents in time series analysis and its solution by covariant Lyapunov vectors (2013)
  3. Maus, A.; Sprott, J.C.: Evaluating Lyapunov exponent spectra with neural networks (2013)
  4. Mera, Maria Eugenia; Morán, Manuel: Error covariance matrix estimation of noisy and dynamically coupled time series (2013)
  5. Nomura, Taishin; Oshikawa, Shota; Suzuki, Yasuyuki; Kiyono, Ken; Morasso, Pietro: Modeling human postural sway using an intermittent control and hemodynamic perturbations (2013)
  6. Çoban, Gürsan; Büyüklü, Ali H.; Das, Atin: A linearization based non-iterative approach to measure the Gaussian noise level for chaotic time series (2012)
  7. Christodoulou, Eleni G.; Sakkalis, Vangelis; Tsiaras, Vassilis; Tollis, Ioannis G.: BrainNetVis: an open-access tool to effectively quantify and visualize brain networks (2011)
  8. 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)
  9. Xie, Xiaoping; Zhao, Xiaohu; Fang, Youtong; Cao, Zhitong; He, Guoguang: Normalized linear variance decay dimension density and its application of dynamical complexity detection in physiological (fMRI) time series (2011)
  10. Young, R.M.B.; Read, P.L.: Erratum to “Flow transitions resembling bifurcations of the logistic map in simulations of the baroclinic rotating annulus” [Physica D 237 (2008) 2251-2262] (2011)
  11. Carrión, I.Marín; Antúnez, E.Arias; Castillo, M.M.Artigao; Canals, J.J.Miralles: Parallel implementations of the false nearest neighbors method to study the behavior of dynamical models (2010)
  12. Guo, Zhihao; Sheikh, Shaya; Al-Najjar, Camelia; Kim, Hyun; Malakooti, Behnam: Mobile ad hoc network proactive routing with delay prediction using neural network (2010)
  13. Humi, Mayer: Assessing local turbulence strength from a time series (2010)
  14. Litak, Grzegorz; Syta, Arkadiusz; Gajewski, Jakub; Jonak, Józef: Detecting and identifying non-stationary courses in the ripping head power consumption by recurrence plots (2010)
  15. Litak, Grzegorz; Wiercigroch, Marian; Horton, Bryan W.; Xu, Xu: Transient chaotic behaviour versus periodic motion of a parametric pendulum by recurrence plots (2010)
  16. Ma, Jun; Li, An-Bang; Pu, Zhong-Sheng; Yang, Li-Jian; Wang, Yuan-Zhi: A time-varying hyperchaotic system and its realization in circuit (2010)
  17. Udrea, Andreea; Olteanu, Mircea: Image analysis based on the study of the attractor of a time series (2010)
  18. Yang, Caixia; Wu, Qiong: On stability analysis via Lyapunov exponents calculated from a time series using nonlinear mapping-a case study (2010)
  19. Zhou, Douglas; Sun, Yi; Rangan, Aaditya V.; Cai, David: Spectrum of Lyapunov exponents of non-smooth dynamical systems of integrate-and-fire type (2010)
  20. Aono, Masashi; Hirata, Yoshito; Hara, Masahiko; Aihara, Kazuyuki: Amoeba-based chaotic neurocomputing: combinatorial optimization by coupled biological oscillators (2009)

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