NetCausality: A time-delayed neural network tool for causality detection and analysis

Abstract The analysis of causality between systems is still an important research activity, which finds application in several fields of science. The software presented is a new tool for causality detection and analysis between time series. The proposed technique is based on time-delayed neural networks (TDNN). The tool is developed in MATLAB and it comprises three main functions. The first one returns the total causality between two or more systems of equations. The second tool is used to find the “time horizon”, id est the time delay at which the influence between the systems occurs. The last function is a causality feature detection to determine the time intervals, in which the mutual coupling is sufficiently strong to have a real influence on the target.

[1]  J. Vega,et al.  Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET , 2018 .

[2]  Norman E. Fenton,et al.  The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie. Basic Books (2018) , 2020, Artif. Intell..

[3]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[4]  Grienggrai Rajchakit,et al.  Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks , 2020, Neurocomputing.

[5]  M. Paluš,et al.  Comparison of six methods for the detection of causality in a bivariate time series. , 2018, Physical review. E.

[6]  A. Seth,et al.  Granger causality for state-space models. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  C. Giroud,et al.  Radiation pattern and impurity transport in argon seeded ELMy H-mode discharges in JET , 2002 .

[8]  M. Francucci,et al.  Application of a CO2 dial system for infrared detection of forest fire and reduction of false alarm , 2007 .

[9]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[10]  Chee Peng Lim,et al.  A Delay-Dividing Approach to Robust Stability of Uncertain Stochastic Complex-Valued Hopfield Delayed Neural Networks , 2020, Symmetry.

[11]  Luca Faes,et al.  Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality , 2015, Neural Networks.

[12]  Michela Gelfusa,et al.  Extensive statistical analysis of ELMs on JET with a carbon wall , 2014 .

[13]  Jinde Cao,et al.  Hybrid Control Scheme for Projective Lag Synchronization of Riemann–Liouville Sense Fractional Order Memristive BAM NeuralNetworks with Mixed Delays , 2019, Mathematics.

[14]  Jinde Cao,et al.  Global Robust Synchronization of Fractional Order Complex Valued Neural Networks with Mixed Time Varying Delays and Impulses , 2019, International Journal of Control, Automation and Systems.

[15]  Jürgen Kurths,et al.  Inferring Indirect Coupling by Means of Recurrences , 2011, Int. J. Bifurc. Chaos.

[16]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[17]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.