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Тест причинности по Грейнджеру×Анализ Количественной Оценки Рекуррентности (АКОР)×
ОбластьЭконометрикаСложные системы
СемействоRegression modelMachine learning
Год появления19692007
Автор методаClive W. J. GrangerMarwan, Romano, Thiel & Kurths
ТипTime-series predictive causality testNonlinear time-series characterization
Основополагающий источникGranger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329. DOI ↗
Другие названияGranger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik TestiRQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi
Связанные52
СводкаThe 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.Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems.
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ScholarGateСравнение методов: Granger Causality · Recurrence Quantification Analysis. Получено 2026-06-18 из https://scholargate.app/ru/compare