An automated algorithm for the generation of dynamically reconstructed trajectories

Chandrasekaran Komalapriya, M Carmen Romano , Marco Thiel, Norbert Marwan, Jurgen Kurths, Istvan Z Kiss, John L Hudson

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The lack of long enough data sets is a major problem in the study of many real world systems. As it has been recently shown [ C. Komalapriya, M. Thiel, M. C. Romano, N. Marwan, U. Schwarz, and J. Kurths, Phys. Rev. E 78, 066217 (2008) ], this problem can be overcome in the case of ergodic systems if an ensemble of short trajectories is available, from which dynamically reconstructed trajectories can be generated. However, this method has some disadvantages which hinder its applicability, such as the need for estimation of optimal parameters. Here, we propose a substantially improved algorithm that overcomes the problems encountered by the former one, allowing its automatic application. Furthermore, we show that the new algorithm not only reproduces the short term but also the long term dynamics of the system under study, in contrast to the former algorithm. To exemplify the potential of the new algorithm, we apply it to experimental data from electrochemical oscillators and also to analyze the well-known problem of transient chaotic trajectories.
Original languageEnglish
Article number013107
Number of pages9
Issue number1
Early online date3 Feb 2010
Publication statusPublished - Mar 2010

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  • chaos
  • electrochemistry
  • oscillations
  • reaction kinetics theory


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