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Nonlinear time-series analysis of Brazilian fisheries data: Entropy, complexity and persistence

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  • Vilhena, Alan N.
  • Pires, Marcelo A.
  • Da Silva, Rone N.
  • Duarte Queirós, Sílvio M.
  • Ribeiro, Ana B.N.
  • Pinto, Erveton P.

Abstract

The ordinal pattern method offers a robust approach for capturing intricate temporal dependencies characteristic of complex systems. Despite its widespread application across diverse scientific domains, the potential of the ordinal methodology for characterizing the time series of fisheries science remains unexplored. We address this gap. Employing a framework based on ordinal measures, including entropy, complexity, and persistence, we elucidate the underlying patterns in the temporal evolution of marine fisheries landings across several Brazilian states. Our findings reveal significant regional variations in artisanal and industrial fishing yields, temporal correlations between these sectors, a notable propensity for the persistence of current trends, and a considerable level of unpredictability. This research highlights the utility of the ordinal pattern method as a valuable toolkit for understanding the intricate dynamics of fisheries systems.

Suggested Citation

  • Vilhena, Alan N. & Pires, Marcelo A. & Da Silva, Rone N. & Duarte Queirós, Sílvio M. & Ribeiro, Ana B.N. & Pinto, Erveton P., 2026. "Nonlinear time-series analysis of Brazilian fisheries data: Entropy, complexity and persistence," Applied Mathematics and Computation, Elsevier, vol. 516(C).
    • Handle: RePEc:eee:apmaco:v:516:y:2026:i:c:s0096300325005892
    DOI: 10.1016/j.amc.2025.129864
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    References listed on IDEAS

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    1. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2009. "Forbidden patterns, permutation entropy and stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2854-2864.
    2. Erveton P. Pinto & Marcelo A. Pires & Rone N. da Silva & S'ilvio M. Duarte Queir'os, 2025. "Cryptocurrency Time Series on the Binary Complexity-Entropy Plane: Ranking Efficiency from the Perspective of Complex Systems," Papers 2504.01974, arXiv.org.
    3. Pinto, Erveton P. & Pires, Marcelo A. & da Silva, Rone N. & Duarte Queirós, Sílvio M., 2025. "Cryptocurrency time series on the Binary Complexity-Entropy Plane: Ranking efficiency from the perspective of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
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