TY  - JOUR
A1  - Ramos, Antonio M. T.
A1  - Builes-Jaramillo, Alejandro
A1  - Poveda, German
A1  - Goswami, Bedartha
A1  - Macau, Elbert E. N.
A1  - Kurths, Jürgen
A1  - Marwan, Norbert
T1  - Recurrence measure of conditional dependence and applications
JF  - Physical review : E, Statistical, nonlinear and soft matter physics
N2  - Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Herewe propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.
Y1  - 2017
U6  - https://doi.org/10.1103/PhysRevE.95.052206
SN  - 2470-0045
SN  - 2470-0053
VL  - 95
PB  - American Physical Society
CY  - College Park
ER  -