विधियों की तुलना करें

	अभिसारी क्रॉस मैपिंग (CCM)	नमूना एन्ट्रॉपी
क्षेत्र	कारणात्मक अनुमान	जटिल तंत्र
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2012	2000
प्रवर्तक	George Sugihara et al.	Richman & Moorman
प्रकार	Nonlinear time-series causality test	Nonlinear entropy measure
मौलिक स्रोत	Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.	Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049.
उपनाम	CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama	SampEn, Sample Entropy (SampEn), Örneklem Entropisi, Nonlinear Complexity Measure
संबंधित	3	2
सारांश	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.	Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity.