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 150 articles , 1 standard article )

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  1. Andreadis, Ioannis; Fragkou, Athanasios D.; Karakasidis, Theodoros E.: On a topological criterion to select a recurrence threshold (2020)
  2. Deshmukh, Varad; Bradley, Elizabeth; Garland, Joshua; Meiss, James D.: Using curvature to select the time lag for delay reconstruction (2020)
  3. González, Amaru; Castillo, Ernesto; Cruchaga, Marcela A.: Numerical verification of a non-residual orthogonal term-by-term stabilized finite element formulation for incompressible convective flow problems (2020)
  4. Rosa, Lucas A. S.; Prebianca, Flavio; Hoff, Anderson; Manchein, Cesar; Albuquerque, Holokx A.: Characterizing the dynamics of the watt governor system under harmonic perturbation and Gaussian noise (2020)
  5. Barrios, Guillermo; Huelsz, Guadalupe; Rechtman, Raúl: Heat transfer and flow transitions of a thermal plume generated by a heating element on the enclosure bottom wall (2019)
  6. Stender, Merten; Di Bartolomeo, Mariano; Massi, Francesco; Hoffmann, Norbert: Revealing transitions in friction-excited vibrations by nonlinear time-series analysis (2019)
  7. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  8. Danca, Marius-F.; Kuznetsov, Nikolay: Matlab code for Lyapunov exponents of fractional-order systems (2018)
  9. Lancaster, Gemma; Iatsenko, Dmytro; Pidde, Aleksandra; Ticcinelli, Valentina; Stefanovska, Aneta: Surrogate data for hypothesis testing of physical systems (2018)
  10. Ma, Huanfei; Leng, Siyang; Chen, Luonan: Data-based prediction and causality inference of nonlinear dynamics (2018)
  11. Muñoz-Pacheco, J. M.; Guevara-Flores, D. K.; Félix-Beltrán, O. G.; Tlelo-Cuautle, E.; Barradas-Guevara, J. E.; Volos, C. K.: Experimental verification of optimized multiscroll chaotic oscillators based on irregular saturated functions (2018)
  12. Alves, Paulo Ricardo L.; Duarte, L. G. S.; da Mota, L. A. C. P.: A new characterization of chaos from a time series (2017)
  13. Christopoulos, Stavros-Richard G.; Sarlis, Nicholas V.: An application of the coherent noise model for the prediction of aftershock magnitude time series (2017)
  14. Huffaker, Ray; Bittelli, Marco; Rosa, Rodolfo: Nonlinear time series analysis with R (2017)
  15. Kanamaru, Takashi: Chaotic pattern alternations can reproduce properties of dominance durations in multistable perception (2017)
  16. McCullough, M.; Small, M.; Iu, H. H. C.; Stemler, T.: Multiscale ordinal network analysis of human cardiac dynamics (2017)
  17. Tumkur, Ravi Kumar R.; Pearlstein, Arne J.; Masud, Arif; Gendelman, Oleg V.; Blanchard, Antoine B.; Bergman, Lawrence A.; Vakakis, Alexander F.: Effect of an internal nonlinear rotational dissipative element on vortex shedding and vortex-induced vibration of a sprung circular cylinder (2017)
  18. Udhayakumar, Radhagayathri K.; Karmakar, Chandan; Palaniswami, Marimuthu: Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal (2017)
  19. Kuznetsov, N. V.: The Lyapunov dimension and its estimation via the Leonov method (2016)
  20. Kuznetsov, N. V.; Alexeeva, T. A.; Leonov, G. A.: Invariance of Lyapunov exponents and Lyapunov dimension for regular and irregular linearizations (2016)

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