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| Convergent Cross Mapping (CCM)× | Analisi Quantitativa delle Ricorrenze (RQA)× | |
|---|---|---|
| Campo≠ | Inferenza causale | Sistemi complessi |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2012 | 2007 |
| Ideatore≠ | George Sugihara et al. | Marwan, Romano, Thiel & Kurths |
| Tipo≠ | Nonlinear time-series causality test | Nonlinear time-series characterization |
| Fonte seminale≠ | Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500. DOI ↗ | Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329. DOI ↗ |
| Alias | CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama | RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi |
| Correlati≠ | 3 | 2 |
| Sintesi≠ | Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, the historical record of Y contains enough information to recover the states of X. Causality is confirmed when cross-map skill improves—converges—as the time-series library grows longer. | Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems. |
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