Temporal fluctuation scaling in nonstationary counting processes

TL;DR

This paper demonstrates that fluctuation scaling laws are observable even in short timescales with few events and introduces a method to extract the scaling exponent from nonstationary data.

Contribution

It shows the fluctuation scaling law applies to short timescales and proposes a systematic method to determine the scaling exponent in nonstationary counting processes.

Findings

Scaling law appears in short timescales with few events

Method to extract the scaling exponent from nonstationary data

Fluctuation scaling observed in nonstationary event sequences

Abstract

The fluctuation scaling law has universally been observed in a wide variety of phenomena. For counting processes describing the number of events occurred during time intervals, it is expressed as a power function relationship between the variance and the mean of the event count per unit time, the characteristic exponent of which is obtained theoretically in the limit of long duration of counting windows. Here I show that the scaling law effectively appears even in a short timescale in which only a few events occur. Consequently, the counting statistics of nonstationary event sequences are shown to exhibit the scaling law as well as the dynamics at temporal resolution of this timescale. I also propose a method to extract in a systematic manner the characteristic scaling exponent from nonstationary data.