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 163 articles , 1 standard article )

Showing results 1 to 20 of 163.
Sorted by year (citations)

1 2 3 ... 7 8 9 next

  1. Arthur A. B. Pessa, Haroldo V. Ribeiro: ordpy: A Python package for data analysis with permutation entropy and ordinal network methods (2021) arXiv
  2. Della Marca, Rossella; d’Onofrio, Alberto: Volatile opinions and optimal control of vaccine awareness campaigns: chaotic behaviour of the forward-backward sweep algorithm vs. heuristic direct optimization (2021)
  3. Pessa, Arthur A. B.; Ribeiro, Haroldo V.: ordpy: a Python package for data analysis with permutation entropy and ordinal network methods (2021)
  4. Pietrych, Lukasz; Sandubete, Julio E.; Escot, Lorenzo: Solving the chaos model-data paradox in the cryptocurrency market (2021)
  5. Silva-Juárez, Alejandro; Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo; Li, Rui: Optimization of the Kaplan-Yorke dimension in fractional-order chaotic oscillators by metaheuristics (2021)
  6. Altuntas, Volkan; Gok, Murat; Kocal, Osman Hilmi: Response of Lyapunov exponents to diffusion state of biological networks (2020)
  7. Andreadis, Ioannis; Fragkou, Athanasios D.; Karakasidis, Theodoros E.: On a topological criterion to select a recurrence threshold (2020)
  8. Deshmukh, Varad; Bradley, Elizabeth; Garland, Joshua; Meiss, James D.: Using curvature to select the time lag for delay reconstruction (2020)
  9. George, Sandip V.; Misra, R.; Ambika, G.: Fractal measures and nonlinear dynamics of overcontact binaries (2020)
  10. 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)
  11. Meng, Xuhui; Karniadakis, George Em: A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems (2020)
  12. Pan, Shaowu; Duraisamy, Karthik: On the structure of time-delay embedding in linear models of non-linear dynamical systems (2020)
  13. 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)
  14. 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)
  15. Stender, Merten; Di Bartolomeo, Mariano; Massi, Francesco; Hoffmann, Norbert: Revealing transitions in friction-excited vibrations by nonlinear time-series analysis (2019)
  16. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  17. Danca, Marius-F.; Kuznetsov, Nikolay: Matlab code for Lyapunov exponents of fractional-order systems (2018)
  18. Jacob, Rinku; Harikrishnan, K. P.; Misra, Ranjeev; Ambika, G.: Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves (2018)
  19. Lancaster, Gemma; Iatsenko, Dmytro; Pidde, Aleksandra; Ticcinelli, Valentina; Stefanovska, Aneta: Surrogate data for hypothesis testing of physical systems (2018)
  20. Ma, Huanfei; Leng, Siyang; Chen, Luonan: Data-based prediction and causality inference of nonlinear dynamics (2018)

1 2 3 ... 7 8 9 next