https://doi.org/10.1140/epjs/s11734-025-02121-w
Regular Article
Energy-efficient recurrence quantification analysis
1
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegrafenberg A31, 14473, Potsdam, Germany
2
Institute of Geoscience, University of Potsdam, Karl-Liebknecht-Straße 32, 14476, Potsdam, Germany
3
Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Straße 32, 14476, Potsdam, Germany
a
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Received:
18
November
2025
Accepted:
18
December
2025
Published online:
12
January
2026
Recurrence quantification analysis (RQA) is a widely used tool for studying complex dynamical systems, but its standard implementation requires computationally expensive calculations of recurrence plots (RPs) and line length histograms. This study introduces strategies to compute RQA measures directly from time series or phase space vectors, avoiding the need to construct RPs. The calculations can be further accelerated and optimised by applying a random sampling procedure, in which only a subset of line structures is evaluated. These modifications result in shorter run times, less memory use and access, and lower overall energy consumption during analysis whilst maintaining accuracy. This makes them especially appealing for large-scale data analysis and machine learning applications. The ideas are not limited to diagonal line measures, but can likewise be applied to vertical line-based measures and to recurrence network measures. By lowering computational costs, the proposed strategies contribute to energy saving and sustainable data analysis, and broaden the applicability of recurrence-based methods in modern research contexts.
© The Author(s) 2026
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