Detection of timescales in evolving complex systems

TL;DR

This paper introduces a scalable, parameter-free method for detecting multiple evolutionary timescales in complex systems by identifying peaks in similarity between consecutive data intervals, applicable to both smooth and abrupt changes.

Contribution

The authors propose a novel, efficient technique to dynamically define time intervals that match the system's evolution, improving over fixed or rate-based methods.

Findings

Successfully detects timescales in toy models with smooth and sharp evolution

Validates method on real datasets demonstrating effectiveness

Scalable to large datasets with high computational efficiency

Abstract

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. Then, one may directly follow how the snapshots evolve in time, or aggregate the snapshots within some time intervals to form representative "slices" of the evolution of the system configuration. This is often done with constant intervals, whose duration is based on arguments on the nature of the system and of its dynamics. A more refined approach would be to consider the rate of activity in the system to perform a separation of timescales. However, an even better alternative would be to define dynamic intervals that match the evolution of the system's configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.