Abstract

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.

Highlights

  • 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 aforementioned methodology has been compared with the application of the Transfer Entropy (TE) and of the Conditional Transfer Entropy (CTE) [16] on the same set of data

  • Be retrieved information related both to the evolution of such instabilities and of the occurrence of the pacing, can by measuring specific physical quantities in the form of time series

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Summary

Multiple Causality between Time Series

Most specialists agree to trace the concept of synchronization back to Huygens and his study on what we define today as antiphase synchronized pendula. The evolution of the generalized and phase synchronizations between two quantities are among the most widely studied, while lag synchronization is often aimed at inferring the delay of the maximal influence between two observables. Convergent Cross Mapping (ECCM) [8] to identify the causal direction, coupling delay between two quantities described by two time series. This article is a contribution to this line of work, providing a new technique for the analysis of the synchronization between two quantities to assess the time delay of causality in presence of external perturbations.

Refinement of Joint Recurrent Plots
Transfer Entropy and Conditional Transfer Entropy
Numerical Tests
Illustrative
Extension to More than Three Observables
Behaviors
Presence of Outliers
Discussions on Lines of Futurein
Conclusions
Behaviors of the
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