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الربط المتقاطع المتقارب (CCM)×تحليل تكرار الكم (RQA)×
المجالالاستدلال السببيالأنظمة المعقدة
العائلةMachine learningMachine learning
سنة النشأة20122007
صاحب الطريقةGeorge Sugihara et al.Marwan, Romano, Thiel & Kurths
النوعNonlinear time-series causality testNonlinear time-series characterization
المصدر التأسيسيSugihara, 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 ↗
الأسماء البديلةCCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz HaritalamaRQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi
ذات صلة32
الملخصConvergent 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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ScholarGateقارن الطرق: Convergent Cross Mapping · Recurrence Quantification Analysis. استُرجع بتاريخ 2026-06-17 من https://scholargate.app/ar/compare