A Refinement of Recurrence Analysis to Determine the Time Delay of Causality in Presence of External Perturbations

This article describes a refinement of recurrence analysis to determine the delay in the causal influence between a driver and a target, in the presence of additional perturbations affecting the time series of the response observable. The methodology is based on the definition of a new type of recurrence plots, the Conditional Joint Recurrence plot. The potential of the proposed approach resides in the great flexibility of recurrence plots themselves, which allows extending the technique to more than three quantities. Autoregressive time series, both linear and nonlinear, with different couplings and percentage of additive Gaussian noise have been investigated in detail, with and without outliers. The approach has also been applied to the case of synthetic periodic signals, representing realistic situations of synchronization experiments in thermonuclear fusion. The results obtained have been very positive; the proposed Conditional Joint Recurrence plots have always managed to identify the right interval of the causal influences and are very competitive with alternative techniques such as the Conditional Transfer Entropy.

[1]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

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

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

[4]  Luis Gerardo de la Fraga,et al.  Sizing CMOS Amplifiers by PSO and MOL to Improve DC Operating Point Conditions , 2020, Electronics.

[5]  Xun Chen,et al.  Complex network analysis of brain functional connectivity under a multi-step cognitive task , 2017, 1711.10376.

[6]  Joseph T. Lizier,et al.  An Introduction to Transfer Entropy , 2016, Springer International Publishing.

[7]  Milan Paluš,et al.  Detection of coupling delay: A problem not yet solved. , 2017, Chaos.

[8]  O.N. Pavlova,et al.  Scaling features of intermittent dynamics: Differences of characterizing correlated and anti-correlated data sets , 2019 .

[9]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

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

[11]  M Palus,et al.  Synchronization as adjustment of information rates: detection from bivariate time series. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[13]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[14]  A Murari,et al.  Mutual interaction of Faraday rotation and Cotton–Mouton phase shift in JET polarimetric measurements. , 2010, The Review of scientific instruments.

[15]  Cristina Masoller,et al.  Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis , 2015, Scientific Reports.

[16]  Joseph T. Lizier,et al.  JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems , 2014, Front. Robot. AI.

[17]  J. Vega,et al.  The influence of an ITER-like wall on disruptions at JET , 2013 .

[18]  N. Marwan,et al.  Nonlinear analysis of bivariate data with cross recurrence plots , 2002, physics/0201061.

[19]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[21]  Jet Efda Contributors,et al.  Investigating pellet ELM triggering physics using the new small size pellet launcher at JET , 2010 .

[22]  Jürgen Kurths,et al.  Multivariate recurrence plots , 2004 .

[23]  A. Murari,et al.  Sawtooth pacing with on-axis ICRH modulation in JET-ILW , 2017 .

[24]  Buncha Munmuangsaen,et al.  On a Simple Single-Transistor-Based Chaotic Snap Circuit: A Maximized Attractor Dimension at Minimized Damping and a Stable Equilibrium , 2019, IEEE Access.

[25]  Yi Zhao,et al.  Reciprocal characterization from multivariate time series to multilayer complex networks. , 2020, Chaos.

[26]  Michela Gelfusa,et al.  How to assess the efficiency of synchronization experiments in tokamaks , 2016 .

[27]  M. Lungaroni,et al.  Application of transfer entropy to causality detection and synchronization experiments in tokamaks , 2016 .

[28]  A. Murari,et al.  The JET programme in support of ITER , 2007 .

[29]  Albert C. J. Luo,et al.  A theory for synchronization of dynamical systems , 2009 .

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

[31]  Michela Gelfusa,et al.  On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals , 2018, Entropy.

[32]  A. Azzalini,et al.  Statistical applications of the multivariate skew normal distribution , 2009, 0911.2093.

[33]  Yu Huang,et al.  Detecting causality from time series in a machine learning framework. , 2020, Chaos.

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

[35]  George Sugihara,et al.  Distinguishing time-delayed causal interactions using convergent cross mapping , 2015, Scientific Reports.

[36]  Charles L. Webber,et al.  Recurrence Quantification Analysis , 2015 .

[37]  Emmanuele Peluso,et al.  On efficiency and interpretation of sawteeth pacing with on-axis ICRH modulation in JET , 2017 .