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  - 
TY  - JOUR
A1  - Agarwal, Ankit
A1  - Marwan, Norbert
A1  - Maheswaran, Rathinasamy
A1  - Merz, Bruno
A1  - Kurths, Jürgen
T1  - Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach
JF  - Nonlinear processes in geophysics
N2  - The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Y1  - 2017
U6  - https://doi.org/10.5194/npg-24-599-2017
SN  - 1023-5809
VL  - 24
SP  - 599
EP  - 611
PB  - Copernicus
CY  - Göttingen
ER  -