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 173 articles , 1 standard article )

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  1. Chen, Jie; Gao, Yi; Liu, Yongming: Multi-fidelity data aggregation using convolutional neural networks (2022)
  2. Arthur A. B. Pessa, Haroldo V. Ribeiro: ordpy: A Python package for data analysis with permutation entropy and ordinal network methods (2021) arXiv
  3. Danca, Marius-F.: Matlab code for Lyapunov exponents of fractional-order systems. II: The noncommensurate case (2021)
  4. 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)
  5. Garcia, Ferran; Seilmayer, Martin; Giesecke, André; Stefani, Frank: Long term time dependent frequency analysis of chaotic waves in the weakly magnetized spherical Couette system (2021)
  6. Osrajnik, Damjan; Grubelnik, Vladimir; Repnik, Robert: Multirhythmicity but no deterministic chaos in vibrating strings (2021)
  7. Pessa, Arthur A. B.; Ribeiro, Haroldo V.: ordpy: a Python package for data analysis with permutation entropy and ordinal network methods (2021)
  8. Pietrych, Lukasz; Sandubete, Julio E.; Escot, Lorenzo: Solving the chaos model-data paradox in the cryptocurrency market (2021)
  9. 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)
  10. Zandi-Mehran, Nazanin; Jafari, Sajad; Hashemi Golpayegani, Seyed Mohammad Reza; Namazi, Hamidreza: The effect of noise and nonlinear noise reduction methods on the fractal dimension of chaotic time series (2021)
  11. Altuntas, Volkan; Gok, Murat; Kocal, Osman Hilmi: Response of Lyapunov exponents to diffusion state of biological networks (2020)
  12. Alves, P. R. L.: Dynamic characteristic of Bitcoin cryptocurrency in the reconstruction scheme (2020)
  13. Andreadis, Ioannis; Fragkou, Athanasios D.; Karakasidis, Theodoros E.: On a topological criterion to select a recurrence threshold (2020)
  14. Deshmukh, Varad; Bradley, Elizabeth; Garland, Joshua; Meiss, James D.: Using curvature to select the time lag for delay reconstruction (2020)
  15. George, Sandip V.; Misra, R.; Ambika, G.: Fractal measures and nonlinear dynamics of overcontact binaries (2020)
  16. 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)
  17. Meng, Xuhui; Karniadakis, George Em: A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems (2020)
  18. Minati, L.; Gambuzza, Lucia V.; Thio, W. J.; Sprott, J. C.; Frasca, Mattia: A chaotic circuit based on a physical memristor (2020)
  19. Pan, Shaowu; Duraisamy, Karthik: On the structure of time-delay embedding in linear models of non-linear dynamical systems (2020)
  20. 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)

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