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arXiv · 1910.08281

Point Process Flows

Nazanin Mehrasa·Ruizhi Deng·Mohamed Osama Ahmed·Bo Chang·Jiawei He·Thibaut Durand·Marcus Brubaker·Greg Mori
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Abstract

Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences.

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Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, Jiawei He, Thibaut Durand, Marcus Brubaker, Greg Mori. 2019-10-18. Point Process Flows. https://arxiv.org/abs/1910.08281

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