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

• Main Authors: Angdembe, Alisha, Iqbal, Wasim A, Hamad, Rebeen Ali, Casement, John, Consortium, AI-Multiply, Missier, Paolo, Reynolds, Nick, Henkin, Rafael, Barnes, Michael R
• Format: Journal Article
• Language: English
• Published: 05-11-2024
• Subjects: Quantitative Biology - Quantitative Methods
• DOI: 10.48550/arxiv.2411.03210

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.