Data-driven discovery of quasiperiodically driven dynamics

TL;DR

This paper introduces a data-driven framework for identifying and modeling quasiperiodically driven dynamical systems from time series data, effectively reconstructing both the driving frequencies and the underlying nonlinear dynamics.

Contribution

It presents a novel approach combining kernel-based harmonic analysis, interpolation, and Koopman operator theory to analyze quasiperiodically driven systems from real-world data.

Findings

Accurately identifies driving frequencies in real-world data

Successfully reconstructs nonlinear dynamics from time series

Applies to systems in astronomy and traffic flow

Abstract

The analysis of a timeseries can provide many new perspectives if it is accompanied by the assumption that the timeseries is generated from an underlying dynamical system. For example, statistical properties of the data can be related to measure theoretic aspects of the dynamics, and one can try to recreate the dynamics itself. The underlying dynamics could represent a natural phenomenon or a physical system, where the timeseries represents a sequence of measurements. In this paper, we present a completely data-driven framework to identify and model quasiperiodically driven dynamical systems (Q.P.D.) from the timeseries it generates. Q.P.D. are a special class of systems that are driven by a periodic source with multiple base frequencies. Such systems abound in nature, e.g., astronomy and traffic flow. Our framework reconstructs the dynamics into two components - the driving quasiperiodic source with generating frequencies; and the driven nonlinear dynamics. We make a combined use of a kernel-based harmonic analysis, kernel-based interpolation technique, and Koopman operator theory. Our framework provides accurate reconstructions and frequency identification for three real-world case studies.