Сравнение методов

	Трансфер-энтропия	Сходящееся перекрестное отображение (CCM)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Machine learning	Machine learning
Год появления	2000	2012
Автор метода	Thomas Schreiber	George Sugihara et al.
Тип	Non-parametric information-theoretic measure	Nonlinear time-series causality test
Основополагающий источник	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.	Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.
Другие названия	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi	CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama
Связанные	3	3
Сводка	Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics.	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.