Salīdzināt metodes

	Pārneses entropija	Grindžera koeficientu pārbaude
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Machine learning	Regression model
Izcelsmes gads	2000	1969
Autors	Thomas Schreiber	Clive W. J. Granger
Tips	Non-parametric information-theoretic measure	Time-series predictive causality test
Pirmavots	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.
Citi nosaukumi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Saistītās	3	5
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.	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.