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Abrupt transitions in time series with uncertainties

Author

Listed:
  • Bedartha Goswami

    (Transdisciplinary Concepts & Methods
    University of Potsdam)

  • Niklas Boers

    (Transdisciplinary Concepts & Methods
    Imperial College London)

  • Aljoscha Rheinwalt

    (Transdisciplinary Concepts & Methods
    University of Potsdam)

  • Norbert Marwan

    (Transdisciplinary Concepts & Methods)

  • Jobst Heitzig

    (Transdisciplinary Concepts & Methods)

  • Sebastian F. M. Breitenbach

    (Ruhr-Universität Bochum)

  • Jürgen Kurths

    (Transdisciplinary Concepts & Methods
    Humboldt-Universität zu Berlin
    Saratov State University)

Abstract

Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Niño-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an ‘uncertainty-aware’ framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.

Suggested Citation

  • Bedartha Goswami & Niklas Boers & Aljoscha Rheinwalt & Norbert Marwan & Jobst Heitzig & Sebastian F. M. Breitenbach & Jürgen Kurths, 2018. "Abrupt transitions in time series with uncertainties," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02456-6
    DOI: 10.1038/s41467-017-02456-6
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    Cited by:

    1. Qiao, Honghai & Deng, Zhenghong & Li, Huijia & Hu, Jun & Song, Qun & Xia, Chengyi, 2021. "Complex networks from time series data allow an efficient historical stage division of urban air quality information," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    2. Ramos, Antônio M.T. & Casagrande, Helder L. & Macau, Elbert E.N., 2020. "Investigation on the high-order approximation of the entropy bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. An, Sufang & An, Feng & Gao, Xiangyun & Wang, Anjian, 2023. "Early warning of critical transitions in crude oil price," Energy, Elsevier, vol. 280(C).
    4. Duan, Wei-Long, 2020. "The stability analysis of tumor-immune responses to chemotherapy system driven by Gaussian colored noises," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    5. Xuefeng Gao & Lingfei Li & Xun Yu Zhou, 2024. "Reinforcement Learning for Jump-Diffusions, with Financial Applications," Papers 2405.16449, arXiv.org, revised Aug 2024.
    6. Cristiane Gea & Luciano Vereda & Eduardo Ogasawara, 2024. "Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1507-1538, September.
    7. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).

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