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| doi=10.1016/j.physrep.2006.11.001
| pages=237
|bibcode = 2007PhR...438..237M }}</ref>. Therefore, the [[recurrence quantification analysis]] quantifies the distribution of these small-scale structures
<ref | author1=J. P. Zbilut | author2=C. L. Webber
| title=Embeddings and delays as derived from quantification of recurrence plots
| journal=Physics Letters A
| volume=171
| issue=3-4
| year=1992
| pages=199–203
| doi=10.1016/0375-9601(92)90426-M
| bibcode = 1992PhLA..171..199Z
| s2cid=122890777
}}</ref><ref>{{cite journal
| author1=C. L. Webber | author2=J. P. Zbilut
| title=Dynamical assessment of physiological systems and states using recurrence plot strategies
| journal=Journal of Applied Physiology
| volume=76
| issue=2
| year=1994
| pages=965-973
| doi=10.1152/jappl.1994.76.2.965
| s2cid=23854540
}}</ref>
<ref name="marwan2008">{{cite journal
| author=N. Marwan
| title=A historical review of recurrence plots
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| url= https://zenodo.org/record/996840
| doi=10.1140/epjst/e2008-00829-1
|bibcode = 2008EPJST.164....3M | arxiv=1709.09971
| s2cid=119494395
}}</ref>. This quantification can be used to describe the recurrence plots in a quantitative way. Applications are classification, predictions, nonlinear parameter estimation, and transition analysis. In contrast to the heuristic approach of the recurrence quantification analysis, which depends on the choice of the embedding parameters, some [[dynamical invariant]]s as [[correlation dimension]], [[K2 entropy]] or [[mutual information]], which are independent on the embedding, can also be derived from recurrence plots. The base for these dynamical invariants are the recurrence rate and the distribution of the lengths of the diagonal lines<ref name="marwan2007"/>. More recent applications use recurrence plots as a tool for time series imaging in machine learning approaches and studying spatio-temporal recurrences<ref name="marwan2023"/>.
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