arXiv:2606.06205v1 Announce Type: new Abstract: Continuous-time event data, in which entities emit instantaneous events over time, arises naturally across many domains such as neuroscience, seismology, and social networks. Non-negative matrix factorization (NMF) is a natural tool to uncover interpretable structure in such data, but it has so far only been applied after binning or smoothing the entity-level counting measures. This preprocessing step comes with the risk of erasing entity-level heterogeneities and fine-grained temporal features. In this paper, we introduce EventNMF, a continuous-
The paper introduces a novel approach to analyzing continuous-time event data, addressing limitations of current methods and improving interpretability.
This development could significantly enhance the understanding and modeling of dynamic systems across various critical domains, from neuroscience and seismology to social networks.
The ability to analyze fine-grained temporal features in event data without prior binning or smoothing will lead to more accurate and granular insights.
- · AI/ML researchers
- · Analytics platforms
- · Neuroscience
- · Seismology
- · Traditional statistical modeling
- · Researchers reliant on binned data analysis
Improved predictive models for complex event-driven systems due to enhanced data analysis capabilities.
Development of new applications and services leveraging fine-grained event data insights for real-time decision making.
Potential for greater societal understanding and control over emergent behaviors in human and natural systems.
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