2006/10/25 by K.-I. Goh, Albert-Ĺaszló Barabási, Goh, Kwang-Il +1 · 10 citations
Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Data Analysis #FOS: Physical sciences #Seismology and Earthquake Studies #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.physics/0610233
openalex publication_date 2006/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The dynamics of a wide range of real systems, from email patterns to earthquakes, display a bursty, intermittent nature, characterized by short timeframes of intensive activity followed by long times of no or reduced activity. The understanding of the origin of such bursty patterns is hindered by the lack of tools to compare different systems using a common framework. We introduce two measures to distinguish the mechanisms responsible for the bursty nature of real signals, changes in the interevent times and memory. We find that while the burstiness of natural phenomena is rooted in both the interevent time distribution and memory, for human dynamics memory is weak, and the bursty character is due to changes in the interevent time distribution. Finally, we show that current models lack in their ability to reproduce the activity pattern observed in real systems, opening up new avenues for future work.