Capturing Dynamics of Time-Varying Data via Topology

TL;DR

This paper introduces crocker stacks, a new topological tool for summarizing and visualizing time-varying metric spaces, with applications in biological aggregation models and properties suitable for machine learning.

Contribution

We propose crocker stacks as a novel, continuous topological summary for dynamic data, enhancing visualization and machine learning applications in time-varying metric spaces.

Findings

Crocker stacks are effective for visualizing dynamic data.

They satisfy a desirable continuity property.

Demonstrated utility in biological aggregation model parameter identification.

Abstract

One approach to understanding complex data is to study its shape through the lens of algebraic topology. While the early development of topological data analysis focused primarily on static data, in recent years, theoretical and applied studies have turned to data that varies in time. A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of birds, can contain a vast amount of information. There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of time-varying metric spaces including vineyards [19], crocker plots [56], and multiparameter rank functions [37]. We then introduce a new tool to summarize time-varying metric spaces: a crocker stack. Crocker stacks are convenient for visualization, amenable to machine learning, and satisfy a desirable continuity property which we prove. We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [58]. Altogether, we aim to bring the broader applied mathematics community up-to-date on topological summaries of time-varying metric spaces.