Spatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes
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Dong, Zheng
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Abstract
As the data volume and complexity in modern applications continue to grow, there is an increasing need in parallel for advanced point process models that can effectively capture intricate event dependencies and dynamics.
This thesis focuses on advancing point process modeling by developing deep influence kernels for spatio-temporal event data. Combining statistical modeling principles with the expressive power of deep learning, the proposed methods effectively capture complex event dependencies, improve model estimation efficiency, and enhance interpretability. The thesis also demonstrates the practicality of deep kernel-based point processes in various real-world applications, such as in modeling COVID-19 transmission dynamics and urban crime events\footnote{Implementations are open-sourced at \url{https://github.com/McDaniel7}}.
I hope that the contributions presented here will not only extend to a broader range of methodological and real-world applications but also inspire future research in the rapidly evolving area of spatio-temporal event modeling and neural point processes.
For instance, when looking from a methodological standpoint, using neural networks as a flexible tool offers an opportunity to investigate more complex, higher-order statistics of point process models. On the application side, the use of neural networks allows the integration of comprehensive external data sources within the statistical frameworks, such as demographic or mobility data, to enhance the realism of real-world implementations. The neural point processes also have the potential to be adopted in controlled experiments for the study of variable effects, providing researchers with diverse options. These models could assist domain experts by suggesting new hypotheses derived from robust statistical perspectives, motivating interdisciplinary collaboration.
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Date
2024-12-05
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Dissertation