Dynamic scaling approach to study time series fluctuations

TL;DR

This paper introduces a novel method that maps time series fluctuations onto a surface-growth model, enabling the use of kinetic roughening theory to analyze, classify, and forecast real-world stochastic time series.

Contribution

It presents a new approach that applies nonequilibrium surface-growth models to analyze stochastic time series fluctuations.

Findings

Many real-world time series follow the Family-Viscek dynamic scaling law.

The approach allows classification and modeling of time series based on surface-growth universality classes.

Forecasting can be improved by understanding the scaling properties of fluctuations.

Abstract

We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of time variable, whereas the physical time is treated as the analog of spatial variable. In this way we found that the fluctuations of many real-world time series satisfy the analog of the Family-Viscek dynamic scaling ansatz. This finding permits to use the powerful tools of kinetic roughening theory to classify, model, and forecast the fluctuations of real-world time series.