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<p>In real-world scenarios, event data often exhibits inherent randomness, complex historical dependencies, and hierarchical spatio-temporal clustering. However, existing neural point process models typically overlook the hierarchical nature of spatial information, or treat temporal and spatial relevance as separate factors. This oversight results in suboptimal performance when handling data with prominent spatio-temporal clustering features. Additionally, current models have not explicitly considered the interdependencies between event types. To remedy these limitations, we introduce a novel neural point process framework named EventFormer. Leveraging the time-oriented and type-oriented multi-head attention modules, along with a Ladder Attention mechanism that progressively refines spatial embeddings across hierarchical levels, EventFormer adeptly captures the nuanced dynamics of event occurrences. Furthermore, EventFormer incorporates a type-aware conditional intensity function to explicitly model interactions between event types, enhancing both predictive accuracy and interpretability. Extensive experiments on real-world datasets demonstrate the outstanding performance of EventFormer in event likelihoods modeling and prediction tasks.</p>

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EventFormer: a hierarchical neural point process framework for spatio-temporal clustering events prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

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  • Shanshan Yu,
  • Danhuai Guo,
  • Yanjie Fu,
  • Wei Jin

摘要

In real-world scenarios, event data often exhibits inherent randomness, complex historical dependencies, and hierarchical spatio-temporal clustering. However, existing neural point process models typically overlook the hierarchical nature of spatial information, or treat temporal and spatial relevance as separate factors. This oversight results in suboptimal performance when handling data with prominent spatio-temporal clustering features. Additionally, current models have not explicitly considered the interdependencies between event types. To remedy these limitations, we introduce a novel neural point process framework named EventFormer. Leveraging the time-oriented and type-oriented multi-head attention modules, along with a Ladder Attention mechanism that progressively refines spatial embeddings across hierarchical levels, EventFormer adeptly captures the nuanced dynamics of event occurrences. Furthermore, EventFormer incorporates a type-aware conditional intensity function to explicitly model interactions between event types, enhancing both predictive accuracy and interpretability. Extensive experiments on real-world datasets demonstrate the outstanding performance of EventFormer in event likelihoods modeling and prediction tasks.