https://doi.org/10.1140/epjs/s11734-022-00715-2
Regular Article
Cross-correlation analysis at multiple resolutions
1
Saratov State University, Astrakhanskaya Str. 83, 410012, Saratov, Russia
2
Regional Scientific and Educational Mathematical Center, Mathematics of Future Technologies, 410012, Saratov, Russia
Received:
23
September
2022
Accepted:
26
October
2022
Published online:
7
November
2022
The paper proposes a multiresolution cross-correlation analysis (MCCA) combining cross-correlation analysis of complex signals with multiresolution wavelet analysis, which provides decomposition of datasets at several resolution levels and processing of the obtained sets of detail wavelet coefficients. Such an approach can improve the interpretation of changes in the dynamics of the system under study, associating the occurring phenomena with the mechanisms responsible for them. The possibilities of MCCA are illustrated by the example of synchronous and asynchronous dynamics of the model of two interacting nephrons providing oscillations with two different time scales.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
