Quantifying Non-Stationarity with Information Theory

TL;DR

This paper introduces an information-theoretic index to measure the non-stationarity and regularity of stochastic processes across multiple scales, applicable to synthetic and real turbulence data.

Contribution

It proposes a novel multi-scale index based on information theory that captures complete dependencies and assesses stationarity and roughness of processes.

Findings

Index effectively distinguishes stationary and non-stationary processes.

Index reveals scale-dependent non-stationarity in turbulence data.

Application to synthetic and experimental turbulence demonstrates practical utility.

Abstract

We introduce an index based on information theory to quantify the stationarity of a stochastic process.The index compares on the one hand the information contained in the increment at the time scale $\tau$ of the process at time $t$ with, on the other hand, the extra information in the variable at time $t$ that is not present at time $t-\tau$. By varying the scale $\tau$, the index can explore a full range of scales. We thus obtain a multi-scale quantity that is not restricted to the first two moments of the density distribution, nor to the covariance, but that probes the complete dependences in the process. This index indeed provides a measure of the regularity of the process at a given scale.Not only is this index able to indicate whether a realization of the process is stationary, but its evolution across scales also indicates how rough and non-stationary it is.We show how the index behaves for various synthetic processes proposed to model fluid turbulence, as well as on experimental fluid turbulence measurements.