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About this paper
Feng Zhou, Quyu Kong, Jie Qiao, Cheng Wan, Yixuan Zhang, Ruichu Cai
The survey organizes temporal point process research on irregularly timed events across Bayesian, neural, and large language model approaches. It explains how these methods model event timing and marks in light of event history, while covering applications and challenges. Across prior studies, neural models often outperform classical models for next-event prediction, but differing evaluation protocols prevent a universal ranking.
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