Σύγκριση μεθόδων

	Συγκλίνουσα Διασταυρούμενη Αντιστοίχιση (CCM)	Δοκιμή Αιτιότητας Granger
Πεδίο	Αιτιακή Συμπερασματολογία	Οικονομετρία
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	2012	1969
Δημιουργός	George Sugihara et al.	Clive W. J. Granger
Τύπος	Nonlinear time-series causality test	Time-series predictive causality test
Θεμελιώδης πηγή	Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.
Εναλλακτικές ονομασίες	CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Συναφείς	3	5
Σύνοψη	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.	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.