Moses, Noah and Joseph Effects in Coupled L\'evy Processes

TL;DR

This paper introduces a method to identify the causes of anomalous diffusion in time-series data by quantifying Joseph, Noah, and Moses effects, and demonstrates its application on coupled Lévy processes.

Contribution

It develops a novel approach to decompose and quantify the roots of anomalous diffusion in coupled Lévy processes using time-series analysis.

Findings

Successfully quantifies the three effects in a coupled Lévy walk model.

Shows convergence of increment distribution to a stable asymptotic shape.

Validates the method against theoretical predictions.

Abstract

We study a method for detecting the origins of anomalous diffusion, when it is observed in an ensemble of times-series, generated experimentally or numerically, without having knowledge about the exact underlying dynamics. The reasons for anomalous diffusive scaling of the mean-squared displacement are decomposed into three root causes: increment correlations are expressed by the "Joseph effect" [Mandelbrot 1968], fat-tails of the increment probability density lead to a "Noah effect" [Mandelbrot 1968], and non-stationarity, to the "Moses effect" [Chen et al. 2017]. After appropriate rescaling, based on the quantification of these effects, the increment distribution converges at increasing times to a time-invariant asymptotic shape. For different processes, this asymptotic limit can be an equilibrium state, an infinite-invariant, or an infinite-covariant density. We use numerical methods of time-series analysis to quantify the three effects in a model of a non-linearly coupled L\'evy walk, compare our results to theoretical predictions, and discuss the generality of the method.