 Efficient computation of recurrence quantification analysis via microstatesLucas Belasque Froguel, Thiago de Lima Prado, Gilberto Corso, Gustavo Zampier dos Santos Lima and Sergio Roberto Lopes Applied Mathematics and Computation, 2022, vol. 428, issue C Abstract: Recurrence plot (RP) is a powerful tool in the study of nonlinear dynamics, being successfully applied in economics, medicine, geophysics, and astronomy. The Recurrence Quantification Analysis (RQA) consists of a methodology to compute RP quantifiers based on statistics over vertical/diagonal recurrent lines, densities, and other features of the RP. The traditional way to calculate the quantifiers computes each recurrent point individually and builds the histogram of the whole RP. Here we propose a new, statistical approach to calculate the quantifiers using the (recurrence) microstates, which are small representative chunks of the RP. The new way of statistically calculating the quantifiers converges fast and brings a computational gain. In particular, it reduces the time complexity from O(K2) to O(K), for K the size of the time-series. Moreover, we show that our results are independent of the system and series size. Keywords: Recurrence quantification analysis; Microstates; Time complexity; Laminarity; Determinism; Efficient (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:428:y:2022:i:c:s0096300322002491 DOI: 10.1016/j.amc.2022.127175 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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