Nonergodisity of a time series obeying L\'evy statistics

TL;DR

This paper investigates the nonergodic behavior of a Le9vy walk time series with power law sojourn times, showing that the autocorrelation functions remain random even over long times, contrasting with traditional ergodic assumptions.

Contribution

It introduces a detailed analysis of nonergodicity in Le9vy processes, providing approximations for the distribution of autocorrelation functions and discussing the nonstationary power spectrum.

Findings

Autocorrelation functions remain random in nonergodic Le9vy processes.

Derived approximations match Monte Carlo simulations.

Discussed nonstationary power spectrum and a generalized Wiener-Khintchine theorem.

Abstract

Time-averaged autocorrelation functions of a dichotomous random process switching between 1 and 0 and governed by wide power law sojourn time distribution are studied. Such a process, called a L\'evy walk, describes dynamical behaviors of many physical systems, fluorescence intermittency of semiconductor nanocrystals under continuous laser illumination being one example. When the mean sojourn time diverges the process is non-ergodic. In that case, the time average autocorrelation function is not equal to the ensemble averaged autocorrelation function, instead it remains random even in the limit of long measurement time. Several approximations for the distribution of this random autocorrelation function are obtained for different parameter ranges, and favorably compared to Monte Carlo simulations. Nonergodicity of the power spectrum of the process is briefly discussed, and a nonstationary Wiener-Khintchine theorem, relating the correlation functions and the power spectrum is presented. The considered situation is in full contrast to the usual assumptions of ergodicity and stationarity.