Detecting limit cycles in stochastic time series

Niels Bohr Institutet

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

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Detecting limit cycles in stochastic time series. / Martiny, Emil S.; Jensen, Mogens H.; Heltberg, Mathias S.

I: Physica A: Statistical Mechanics and its Applications, Bind 605, 127917, 17.02.2022.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Martiny, ES, Jensen, MH & Heltberg, MS 2022, 'Detecting limit cycles in stochastic time series', Physica A: Statistical Mechanics and its Applications, bind 605, 127917. https://doi.org/10.1016/j.physa.2022.127917

APA

Martiny, E. S., Jensen, M. H., & Heltberg, M. S. (2022). Detecting limit cycles in stochastic time series. Physica A: Statistical Mechanics and its Applications, 605, [127917]. https://doi.org/10.1016/j.physa.2022.127917

Vancouver

Martiny ES, Jensen MH, Heltberg MS. Detecting limit cycles in stochastic time series. Physica A: Statistical Mechanics and its Applications. 2022 feb. 17;605. 127917. https://doi.org/10.1016/j.physa.2022.127917

Author

Martiny, Emil S. ; Jensen, Mogens H. ; Heltberg, Mathias S. / Detecting limit cycles in stochastic time series. I: Physica A: Statistical Mechanics and its Applications. 2022 ; Bind 605.

Bibtex

@article{64bd643d58fb4a12b3dc67225ce1613f,

title = "Detecting limit cycles in stochastic time series",

abstract = "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.",

keywords = "Limit cycles, Oscillations, Statistical test, Stochastic dynamics",

author = "Martiny, {Emil S.} and Jensen, {Mogens H.} and Heltberg, {Mathias S.}",

note = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = feb,

day = "17",

doi = "10.1016/j.physa.2022.127917",

language = "English",

volume = "605",

journal = "Physica A: Statistical Mechanics and its Applications",

issn = "0378-4371",

publisher = "Elsevier BV * North-Holland",

}

RIS

TY - JOUR

T1 - Detecting limit cycles in stochastic time series

AU - Martiny, Emil S.

AU - Jensen, Mogens H.

AU - Heltberg, Mathias S.

PY - 2022/2/17

Y1 - 2022/2/17

N2 - 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.

AB - 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.

KW - Limit cycles

KW - Oscillations

KW - Statistical test

KW - Stochastic dynamics

U2 - 10.1016/j.physa.2022.127917

DO - 10.1016/j.physa.2022.127917

M3 - Journal article

AN - SCOPUS:85135690793

VL - 605

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

M1 - 127917

ER -

ID: 343301816