2015
DOI: 10.1063/1.4937164
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Multiscale recurrence analysis of spatio-temporal data

Abstract: The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for … Show more

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Cited by 14 publications
(9 citation statements)
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“…Several extensions of RP techniques for spatially distributed data have been introduced. In this respect, Riedl et al [12] proposed multi-scale recurrence analysis of spatio-temporal data as a way to take spatial patterns of different scales and with different rhythms into account. They showed that this approach is able to both separate mixed regular patterns of specific scales and rhythms and also to reveal largescale rhythms beyond the dominant small-scale dynamics in spatial distributed systems.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Several extensions of RP techniques for spatially distributed data have been introduced. In this respect, Riedl et al [12] proposed multi-scale recurrence analysis of spatio-temporal data as a way to take spatial patterns of different scales and with different rhythms into account. They showed that this approach is able to both separate mixed regular patterns of specific scales and rhythms and also to reveal largescale rhythms beyond the dominant small-scale dynamics in spatial distributed systems.…”
Section: Discussionmentioning
confidence: 99%
“…Prado et al [11] adapted these approaches for spatial RQA to study digital mammography high-resolution images, and showed that subtle details could be highlighted, which may elude visual inspections. Riedl et al [12] proposed to measure similarity between spatially distributed data instances at different time points by means of a mapogram (a special form of a spatiogram), and used the similarity values of the pairwise comparison to construct an RP. This allows to focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics.…”
Section: Background On Multi-variate and Spatial Recurrence Plotsmentioning
confidence: 99%
“…In particular, complex network analysis of time series 19 has been well developed and it contributes greatly to solve challenging problems in different research fields. As one of the complex network-based time series analysis methods, recurrence network [20][21][22][23][24][25][26][27] has been proved to be a powerful approach for characterizing the complicated dynamical behaviors from nonlinear time series. In our previous works, we mainly explored the nonlinear dynamical behaviors underlying distinct gas-liquid flow patterns from different research perspectives, i.e., the perspectives of multiscale analysis 11 and modality transition, 28 respectively.…”
Section: Introductionmentioning
confidence: 99%
“…Shannon entropy of recurrence times is called Recurrence plot density entropy (RPDE) [31], which is found to be very effective to calculate the degree of complexity of the phase space. However, a multiscale approach [25][26][27][28][29][30] of the RPDE has not been explored so far, which is expected to reflect the dynamical characteristics of complex systems more accurately.…”
Section: Introductionmentioning
confidence: 99%