Salīdzināt metodes

	Pārneses entropija	Konverģentā krusteniskā kartēšana (CCM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Machine learning	Machine learning
Izcelsmes gads	2000	2012
Autors	Thomas Schreiber	George Sugihara et al.
Tips	Non-parametric information-theoretic measure	Nonlinear time-series causality test
Pirmavots	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.
Citi nosaukumi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi	CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama
Saistītās	3	3
Kopsavilkums	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.