bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data

TL;DR

bursty_dynamics is a Python package designed to analyze complex temporal patterns in longitudinal data by quantifying burstiness and memory, detecting event clusters, and providing visualization tools for diverse research fields.

Contribution

the package introduces novel methods for measuring burstiness and temporal dependencies, along with event train detection, enhancing analysis of irregular temporal data.

Findings

enables quantification of bursty dynamics in longitudinal data

provides tools for event cluster detection within specified intervals

facilitates visualization and interpretation of temporal patterns

Abstract

Understanding the temporal properties of longitudinal data is critical for identifying trends, predicting future events, and making informed decisions in any field where temporal data is analysed, including health and epidemiology, finance, geosciences, and social sciences. Traditional time-series analysis techniques often fail to capture the complexity of irregular temporal patterns present in such data. To address this gap, we introduce bursty_dynamics, a Python package that enables the quantification of bursty dynamics through the calculation of the Burstiness Parameter (BP) and Memory Coefficient (MC). In temporal data, BP and MC provide insights into the irregularity and temporal dependencies within event sequences, shedding light on complex patterns of disease aetiology, human behaviour, or other information diffusion over time. An event train detection method is also implemented to identify clustered events occurring within a specified time interval, allowing for more focused analysis with reduced noise. With built-in visualisation tools, bursty_dynamics provides an accessible yet powerful platform for researchers to explore and interpret the temporal dynamics of longitudinal data. This paper outlines the core functionalities of the package, demonstrates its applications in diverse research domains, and discusses the advantages of using BP, MC, and event train detection for enhanced temporal data analysis.