arXiv subjects

Dan Mønster

Publications and source records attributed to Dan Mønster.

4 recordsLinked to original sources

arXiv2025

Multivariate Joint Recurrence Quantification Analysis: detecting coupling between time series of different dimensionalities

One challenge with the analysis of complex systems and the interaction between such systems is that they are composed of different numbers of components, or simply the fact that a different number of observables is available for each system. The challenge is how to analyze the interaction of two systems which are not described by the same number of variables. Here, we present multivariate joint recurrence quantification analysis (MvJRQA), a recurrence-based technique that allows to analyze coupling properties between multivariate datasets that differ in dimensionality (i.e., number of observables) and type of data (such as nominal or interval-scaled, for example). First, we introduce the methods, and test it on simulated data from linear and nonlinear systems. Then we apply it to an empirical dataset of EEG and eye tracking data. We introduce the joint recurrence coupling indicator (JRCI) as a measure to assess and compare coupling between systems. Finally, we discuss practical issues regarding the application of the method.

physics.data-an↗

arXiv2020

Aggression heuristics underlie animal dominance hierarchies and provide evidence of group-level social information

Members of a social species need to make appropriate decisions about who, how, and when to interact with others in their group. However, it has been difficult for researchers to detect the inputs to these decisions and, in particular, how much information individuals actually have about their social context. We present a new method that can serve as a social assay to quantify how patterns of aggression depend upon information about the ranks of individuals within social dominance hierarchies. Applied to existing data on aggression in 172 social groups across 85 species in 23 orders, it reveals three main patterns of rank-dependent social dominance: the downward heuristic (aggress uniformly against lower-ranked opponents), close competitors (aggress against opponents ranked slightly below self), and bullying (aggress against opponents ranked much lower than self). The majority of the groups (133 groups, 77%) follow a downward heuristic, but a significant minority (38 groups, 22%) show more complex social dominance patterns (close competitors or bullying) consistent with higher levels of social information use. These patterns are not phylogenetically constrained and different groups within the same species can use different patterns, suggesting that heuristics use may depend on context and the structuring of aggression by social information should not be considered a fixed characteristic of a species. Our approach provides new opportunities to study the use of social information within and across species and the evolution of social complexity and cognition.

q-bio.PE↗

arXiv2020

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series, and to capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version our crqa 2.0 package. This package includes implementations of several recent advances in recurrence-based analysis, among them applications to multivariate data, and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.

physics.data-an↗

arXiv2016

Inferring causality from noisy time series data

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.

nlin.CD↗