Detecting limit cycles in stochastic time series

Research output: Contribution to journalJournal articleResearchpeer-review

The emergence of oscillatory behaviour represents fundamental information about the interactions of the underlying system. In biological systems, oscillations have been observed in experimental data, but due to the significant level of noise, it is difficult to characterize whether observed dynamics based on time series, are truly limit cycles. Here, we present a simple three step method to identify the presence of limit cycles in stochastic systems. Considering input from one-dimensional time series, as are typically obtained in experiments, we propose statistical measures to detect the existence of limit cycles. This is tested on models from chemical networks, and we investigate how the underlying dynamics can be separated depending on the noise level and length of the series.

Original languageEnglish
Article number127917
JournalPhysica A: Statistical Mechanics and its Applications
Number of pages10
Publication statusPublished - 17 Feb 2022

Bibliographical note

Publisher Copyright:
© 2022

    Research areas

  • Limit cycles, Oscillations, Statistical test, Stochastic dynamics

ID: 343301816