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Interpolation of non-uniformly sampled data for recurrence quantification analysis (RQA) can bias results. This study analyzes RQA measure differences and proposes a correction scheme for integer interpolation ratios to improve data analysis accuracy.

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Area of Science:

  • Complex Systems Analysis
  • Data Science
  • Time Series Analysis

Background:

  • Recurrence plots and recurrence quantification analysis (RQA) are vital for studying complex system dynamics, periodicity, and regime changes across diverse scientific fields.
  • Standard RQA requires uniformly sampled data, posing challenges for non-uniform datasets common in medicine, paleoclimatology, and astrophysics.
  • Data interpolation is a common solution but can introduce bias into RQA measures, especially those sensitive to line structures in recurrence plots.

Purpose of the Study:

  • To systematically analyze the impact of different sampling and interpolation methods on RQA measures, specifically the average diagonal line length.
  • To investigate the influence of interpolation sampling rates on RQA measures for real-world, non-uniformly sampled data.
  • To develop and propose a correction scheme to mitigate bias introduced by data interpolation in RQA.

Main Methods:

  • Utilized prototypical model systems to conduct a systematic analysis of RQA measure differences under varying sampling and interpolation conditions.
  • Applied interpolation techniques to non-uniformly sampled real-world datasets (e.g., medical, paleoclimate, astrophysical data).
  • Evaluated the average diagonal line length measure from recurrence plots for data processed with different interpolation strategies.

Main Results:

  • Demonstrated that interpolation can introduce significant bias into RQA measures, particularly affecting the average diagonal line length.
  • Showed that the behavior of the average diagonal line length in real data is highly dependent on the chosen sampling rate during interpolation.
  • Identified that a proposed correction scheme can effectively correct interpolation-induced bias when the interpolation ratio is an integer.

Conclusions:

  • Interpolation of non-uniformly sampled data for RQA requires careful consideration of sampling rates to avoid introducing bias.
  • The developed correction scheme offers a viable method to improve the accuracy of RQA measures derived from interpolated data with integer ratios.
  • This work provides a crucial advancement for applying RQA to challenging, non-uniformly sampled datasets across various scientific disciplines.