AI Article Synopsis
- * The proposed method simplifies recurrence analysis by creating compact recurrence plots with automated parameter selection and increased robustness to noise.
- * This approach enhances the applicability of recurrence analysis for complex systems, including large datasets and machine learning contexts, with promising results demonstrated on both synthetic and real data.
Article Abstract
Recurrence analysis applications are hindered by several issues including the selection of critical parameters, noise sensitivity, computational complexity, or the analysis of non-stationary systems. Great progresses have been made by the community to address these issues individually, yet the diversity of resulting techniques with often additional parameters as well as a lack of consensus still impedes its use by nonspecialists. We present a procedure for simplified recurrence analysis based on compact recurrence plots with automatized parameter selection and enhanced noise robustness, and that are suited to the analysis of complex non-stationary systems. This approach aims at supporting the expansion of recurrence analysis for currently challenging or future applications such as for large systems, on-site studies, or using machine learning. The method is demonstrated on both synthetic and real data showing promising results.
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| http://dx.doi.org/10.1063/5.0225465 | DOI Listing |
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